Factors that influence debt financing of municipal owned entities: a case
study of the city of Johannesburg
by
OUPA MADALA GALANE
submitted in accordance with the requirements
for the degree of
MASTER OF PHILOSOPHY
in the subject
ACCOUNTING SCIENCES
at the
UNIVERSITY OF SOUTH AFRICA
SUPERVISOR: MR S NDLOVU
CO-SUPERVISOR: MR R K NZHINGA
JULY 2019
DECLARATION
I, Oupa Madala Galane student number 36971227, declare that the research
study entitled Factors that influence debt financing of municipal owned
entities: A case study of the City of Johannesburg is my own work and that
all the sources that I have used or quoted have been indicated and acknowledged
by means of complete references.
Moreover, I declare that I have not previously submitted this work for examination
at any other institution of higher learning or UNISA.
Oupa Madala Galane
19 July 2019
SIGNED
DATE
ii
ACKNOWLEDGEMENTS
Glory be to God Almighty, the Creator of heaven and earth, “He who loved the
world so much that He gave his only begotten Son Jesus Christ of Nazareth, that
whoever believe in Him should not perish, but have an everlasting life” (John 3;
16). I draw much inspiration and comfort from the word of God in the book of
Jeremiah 29: 11 that says, “For I know the plans I have for you,” declares the
Lord, “plans to prosper you and not to harm you, plans to give you hope and a
future”.
I would like to express my sincere and special gratitude to my supervisors, Mr
Stephen Ndlovu and Rendani Nzhinga, for guiding and mentoring me throughout
the journey of this project and making it a success and for believing in me and for
their continuous support and words of encouragement.
I am deeply indebted to and dedicate this dissertation to the love of my life,
Hleziphe Galane, and my two children, Bokang and Letago Galane. If it were not
for your support this dissertation would not have be a success, and I am grateful
to God that you are part of my life.
I would also like to thank Pastor Jack Chokwe for language editing this dissertation, Mr George Mogaladi, Prof Joseph Chisasa and Ms Faustina Masocha for
their support in this dissertation, and special thanks to my mother, Glad Seepane,
and the rest of my family for their support.
Thanks also to Mrs. Jenny Seagreen, for the formatting, layout and technical
editing of this research document.
Lastly, I would like to thank the University of South Africa and the College of
Accounting for giving me this opportunity.
Let the grace of the Lord Jesus Christ of Nazareth be with God’s people. Amen!
iii
ABSTRACT
This quantitative research was conducted to examine factors that have an
influence on the debt financing of MoEs for the CoJ. It is quite evident from
previous literatures that there has been a plethora of research undertaken on
debt financing. Despite this, there has been very limited, or no, study conducted
in South Africa especially on MoEs. The main objective of the study was to
determine the factors that influence the debt financing of MoEs. To achieve this,
both descriptive and correlational research designs were used, as well as panel
data regression models. Data was collected from 13 MoEs under the CoJ from
2011 to 2015; moreover, secondary data was used as the main sources and
extracted from the integrated annual reports.
The study tested hypotheses on independent variables. The results show that
profitability has a significant negative relationship with debt financing. On the
other hand, the results revealed that coefficients such as interest rate
(insignificant)
size
(insignificant),
economic
growth
(significant),
age
(insignificant) and business risk (significant) have a positive association with debt
financing. Results further show that borrowing was consistent throughout the
study period, with MoEs relying more on short-term debt than on long-term debt.
The study also concluded that during the period of the study, some entities were
technically insolvent while others were at high risk of being liquidated. The
findings are expected to contribute to the body of knowledge and play a pivotal
role in Municipal Finance Management Act, no 56 of 2003 of National Treasury
policy and decision making with regard to Local Government Municipal Finance
Management Act 56 of 2003, particularly in relation to the parent municipality as
well as its entities. More research needs to be conducted to investigate factors
that influence investment decisions of MoEs/SOEs in South Africa and compare
the various types of debt financing using larger sample size over a longer period.
KEY WORDS:
Capital structure; corporate financing decision; debt financing; debt level;
financial leverage; long-term debt; municipal finance; municipal-owned entities;
short-term debt; total debt.
iv
LIST OF ACRONYMS AND ABBREVIATIONS
AICPA
American Institute of Certified Public Accountants
AGSA
Auditor General South Africa
AGE
Entity age
BR
Business risk
BKCY
Probability of bankruptcy
CoJ
City of Johannesburg
CoGTA
Department of Cooperative Governance in the Ministry of
Cooperative Governance and Traditional Affairs
ENG
Economic growth
EViews
Econometric views
FMA
Financial Management Act
GA
Growth opportunity of the entity
CEO
Chief Executive Officer
CFO
Chief Financial Officer
GDP
Gross domestic product
GROW
Entity growth
INF
Inflation rate
INT
Interest rate
IPO
Initial public purchase
LDL
Long-term debt leverage
LS
Least square
LD
Long-term debt
MFMA
Municipal Financial Management Act
MMA
Municipality Management Act
MoE
Municipal-owned entity
MSA
Municipal Systems Act
v
M&M
Modigliani and Miller
N
Number of observations
OECD
Organisation for Economic Cooperation and Development
OLS
Ordinary least squares
PEM
Public Expenditure Management
PFMA
Public Financial Management Act
POPI Act
Protection of Personal Information Act
PROF
Profitability
PWC
Price Waterhouse Coopers
SAI
Supreme Audit Institution
SAIRB
South African Internal Registered Bond
SAMWU
South African Municipal Workers Union
SD
Short-term debt
Size
Entity size
SOE
State-owned entities
SPSS
Statistical Package for Social Sciences
STATA
Data Analysis and Statistical Software
Std.
Standard
TAX
Corporate tax
TANG
Entity nature of assets (tangibility)
TD
Total debt
TDA
Total debt ratio
vi
CONTENTS
Page
DECLARATION .................................................................................................. ii
ACKNOWLEDGEMENTS .................................................................................. iii
ABSTRACT ....................................................................................................... iv
LIST OF ACRONYMS AND ABBREVIATIONS ................................................. v
CONTENTS ...................................................................................................... vii
LIST OF FIGURES ........................................................................................... xii
LIST OF TABLES ............................................................................................. xii
CHAPTER 1: STUDY ORIENTATION ...............................................................1
1.1
1.2
INTRODUCTION ......................................................................................1
BACKGROUND OF THE STUDY .............................................................1
1.2.1 The structure of debt financing for MoEs .....................................3
1.2.2 The role and relevancy of debt capital structure ..........................6
1.3
PROBLEM STATEMENT .........................................................................7
1.4
RESEARCH QUESTIONS........................................................................9
1.5
RESEARCH OBJECTIVES ......................................................................9
1.6
RESEARCH HYPOTHESES .................................................................. 10
1.7
SIGNIFICANCE OF THE STUDY ........................................................... 11
1.8
RESEARCH METHODOLOGY .............................................................. 11
1.8.1 Research design ........................................................................ 12
1.8.2 Target population ....................................................................... 12
1.8.3 Sampling design ........................................................................ 13
1.8.4 Measuring instrument ................................................................ 13
1.8.5 Data collection ........................................................................... 13
1.8.5.1 Primary data ................................................................ 13
1.8.5.2 Secondary data ........................................................... 14
1.8.6 Data analysis ............................................................................. 14
1.8.7 Anonymity and confidentiality..................................................... 14
1.8.8 Validity and reliability ................................................................. 15
1.9
ETHICAL CONSIDERATIONS ............................................................... 15
1.10 DELIMITATION AND LIMITATIONS....................................................... 15
1.10.1 Delimitation ................................................................................ 15
1.10.2 Limitations .................................................................................. 16
1.11 DEFINITIONS OF TERMS AND CONCEPTS ........................................ 16
1.11.1 Municipal-owned entity (MoE) .................................................... 16
1.11.2 Capital structure ......................................................................... 17
1.11.3 Debt financing ............................................................................ 17
1.11.4 Financing ................................................................................... 17
vii
1.11.5 Financial Leverage..................................................................... 17
1.11.6 Profitability ................................................................................. 17
1.12 THESIS STATEMENT ............................................................................ 17
1.13 OUTLINE OF THE STUDY ..................................................................... 18
CHAPTER 2: THEORETICAL FRAMEWORK ................................................ 19
2.1
2.2
2.3
2.4
2.5
2.5
INTRODUCTION .................................................................................... 19
CAPITAL STRUCTURE.......................................................................... 19
LOCAL GOVERNMENT ......................................................................... 20
PUBLIC FINANCIAL MANAGEMENT ACT ............................................ 21
2.4.1 Public financial management functions ...................................... 23
2.4.2 Municipal Systems Act (MSA) 32 of 2000 as amended ............. 28
2.4.3 Municipal Finance Management Act (MFMA) of 2003 ............... 29
2.4.4 Municipal borrowings ................................................................. 30
2.4.5 Municipal-owned enterprise (MoE) borrowings .......................... 30
SOURCES OF FINANCING FOR MoEs................................................. 31
2.5.1 Equity financing.......................................................................... 32
2.5.2 Debt financing ............................................................................ 33
CONCLUSION ........................................................................................ 35
CHAPTER 3: LITERATURE REVIEW ............................................................. 36
3.1
3.2
3.3
3.4
3.5
INTRODUCTION .................................................................................... 36
BACKGROUND AND DEVELOPMENT OF DEBT FINANCING.............. 36
DEBT FINANCING ................................................................................. 37
THEORIES OF DEBT FINANCING ........................................................ 38
3.4.1 Trade-off theory ......................................................................... 39
3.4.1.1 Static trade-off theory .................................................. 40
3.4.1.2 Dynamic trade-off theory ............................................. 41
3.4.2 Pecking order theory .................................................................. 41
3.4.3 Agency theory ............................................................................ 43
3.4.4 Market timing theory .................................................................. 43
3.4.5 Free cash flow............................................................................ 44
FACTORS INFLUENCING DEBT FINANCING ...................................... 45
3.5.1 Common factors with the entities ............................................... 46
3.5.1.1 Entity size .................................................................... 46
3.5.1.2 Age of the entity .......................................................... 47
3.5.1.3 Profitability................................................................... 48
3.5.1.4 Business risk ............................................................... 49
3.5.1.5 Corporate tax rate ....................................................... 50
3.5.1.6 Entity’s tangible assets ................................................ 51
3.5.1.8 Growth opportunity ...................................................... 52
3.5.2 Macro-economic factors ............................................................. 52
3.5.2.1 Inflation rate ................................................................ 53
3.5.2.2 Gross domestic product (GDP) ................................... 53
viii
3.6
3.7
3.8
3.9
3.5.2.3 Interest rate ................................................................. 54
3.5.2.4 Fiscal policy................................................................. 55
3.5.2.5 Economic growth ......................................................... 56
TYPES OF DEBT FINANCING WITHIN MoEs ....................................... 56
3.6.1 Public and private debt .............................................................. 56
3.6.2 Long-term and short-term debt .................................................. 58
3.6.3 Other debt categories ................................................................ 58
DEBT FINANCING LEVELS WITHIN THE ENTITIES ............................ 59
3.7.1 Book leverage ............................................................................ 59
3.7.2 Market leverage ......................................................................... 60
EMPIRICAL FINDINGS .......................................................................... 60
3.8.1 Empirical findings on variables influencing debt financing
of an entity ................................................................................. 61
3.8.2 Empirical findings on the macro-economic factors ..................... 61
3.8.3 Empirical findings on levels of debt financing within the
entity .......................................................................................... 62
CONCLUSION ........................................................................................ 63
CHAPTER 4: METHODOLOGY ...................................................................... 64
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
INTRODUCTION .................................................................................... 64
RESEARCH PARADIGMS ..................................................................... 64
4.2.1 Positivism approach ................................................................... 65
RESEARCH DESIGN ............................................................................. 65
METHODOLOGICAL APPROACHES .................................................... 66
TARGET POPULATION AND SAMPLING ............................................. 68
4.5.1 Sample design ........................................................................... 68
DATA COLLECTION AND PROCEDURES ........................................... 69
4.6.1 Source of secondary data .......................................................... 70
MEASUREMENT OF VARIABLES ......................................................... 70
4.7.1 Measurement of the dependent variable .................................... 70
4.7.2 Measurement of explanatory (independent) variables ............... 71
4.7.2.1 Entity size .................................................................... 72
4.7.2.2 Age of the entity .......................................................... 72
4.7.2.3 Profitability................................................................... 72
4.7.2.4 Business risk ............................................................... 73
4.7.2.5 Corporate tax .............................................................. 73
4.7.2.6 Assets tangibility ......................................................... 73
4.7.2.7 Growth opportunity (entity) .......................................... 74
4.7.2.8 Inflation and interest rate ............................................. 74
4.7.2.9 Economic growth ......................................................... 75
RESEARCH MODEL .............................................................................. 76
4.8.1 Model for factors influencing debt financing ............................... 76
4.8.2 Model to analyse the different types of debts ............................. 77
4.8.3 Model to determine the extent of debt financing ........................ 77
ix
4.9
4.10
4.11
4.12
4.13
4.14
4.15
4.16
HYPOTHESES DEVELOPMENT ........................................................... 78
4.9.1 Factors influencing debt financing.............................................. 78
METHOD OF DATA ANALYSIS ............................................................. 80
RELIABILITY AND VALIDITY ................................................................. 81
DELIMITATIONS .................................................................................... 82
LIMITATIONS ......................................................................................... 82
ANONYMITY AND CONFIDENTIALITY ................................................. 82
ETHICAL CONSIDERATIONS ............................................................... 83
CONCLUSION ........................................................................................ 83
CHAPTER 5: DATA ANALYSIS AND DISCUSSION OF RESULTS .............. 84
5.1
5.2
5.3
5.4
5.5
5.6
5.7
INTRODUCTION .................................................................................... 84
DIFFERENT TYPES OF DEBT .............................................................. 84
5.2.1 Different types of debt used by MoEs within the CoJ ................. 84
THE EXTENT OF DEBT SUBSTANTIVELY PREFERRED BY
MoEs FOR THE COJ.............................................................................. 87
THE ACCEPTABLE LEVEL OF DEBT WITHIN MoEs FOR COJ........... 89
REGRESSION RESULTS ON FACTORS INFLUENCING DEBT
FINANCING ............................................................................................ 90
5.5.1 Regression analysis of descriptive statistics on factors
influencing debt financing .......................................................... 90
5.5.2 Results from Spearman Correlation analysis ............................. 94
PANEL DATA ......................................................................................... 97
5.6.1 Panel data regression results .................................................... 97
5.6.2 Empirical results entity specific factors .................................... 101
CONCLUSION ...................................................................................... 106
CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS ....................... 107
6.1
6.2
6.3
6.4
6.5
6.6
INTRODUCTION .................................................................................. 107
SUMMARY OF THE OF THE STUDY .................................................. 107
FINDINGS REGARDING THE OBJECTIVES OF THE STUDY ........... 107
6.3.1 Different types of debt financing used by MoEs within
the CoJ .................................................................................... 109
6.3.2 The extent of debt substantively preferred by MoEs and the
acceptable level of debt for MoEs ............................................ 111
6.3.3 Factors influencing debt financing of MoEs within the CoJ ........ 112
CONTRIBUTION OF THE STUDY ....................................................... 115
6.4.1 Factors influencing debt financing within MoEs for the
CoJ .......................................................................................... 115
6.4.2 The types of debt used by the various MoEs ........................... 116
6.4.3 The extent of debt substantively preferred and acceptable
debt level by MoEs................................................................... 116
LIMITATION OF THE STUDY .............................................................. 117
RECOMMENDATIONS ........................................................................ 118
x
6.7
6.8
AREAS FOR FUTURE RESEARCH..................................................... 119
CONCLUSION ...................................................................................... 120
REFERENCES................................................................................................ 121
APPENDICES
APPENDIX A:
ETHICAL CLEARANCE CERTIFICATE .............................. 142
APPENDIX B:
LANGUAGE EDITING CERTIFICATE ................................. 145
APPENDIX C:
TURNITIN REPORT ............................................................ 148
xi
LIST OF TABLES
Table 1.1:
Table 4.1:
Table 4.2:
City of Johannesburg municipal-owned entities database .............5
Financial statements data ............................................................ 69
Debt-financing (financial leverage) as a dependent variable
using various proxies ................................................................... 70
Table 4.3: Independent variables which include internal and
macroeconomic factors ................................................................ 71
Table 5.1: Descriptive statistics of types of debt of MoEs for the CoJ........... 85
Table 5.2: Types of debt between 2011 and 2015 expressed in
percentages ................................................................................. 86
Table 5.3: Descriptive statistics for items of common-size statement of
financial position from 2011 to 2015 ............................................ 88
Table 5.4: Common-size statements of financial position from 2011
to 2015 ......................................................................................... 89
Table 5.5: Descriptive statistics..................................................................... 91
Table 5.6: Explanation of the variables in Table 5.5 above for ease of
reference ...................................................................................... 91
Table 5.7: Correlations analysis .................................................................... 95
Table 5.8: Panel data regressions ................................................................ 98
Table 5.9: Optimal panel data regression ..................................................... 99
Table 5.10: Panel data EGLS (Period SUR) regression ............................... 100
Table 5.11: Hypothesis predictions vs outcomes .......................................... 101
LIST OF FIGURES
Figure 4.1: Gross domestic product ............................................................... 75
Figure 6.1: Total liabilities of MoEs .............................................................. 110
xii
CHAPTER 1
STUDY ORIENTATION
1.1
INTRODUCTION
The objective of this study was to investigate factors that influence the debt
financing of municipal entities (MoEs) with a focus on the City of Johannesburg
(CoJ). Moreover, the study has sought to demonstrate the types of debt used by
MoEs, to determine the extent of debt financing substantively preferred by MoEs
as well as the acceptable levels of debt of MoEs for the CoJ.
The intention of this chapter is to provide an overview of the study, outlining the
research problem, research objective, and research questions guiding the study. It
further discusses the significance of the study by explaining the research
methodology applied and presented the outline of the study.
1.2
BACKGROUND OF THE STUDY
Municipal-owned entities (MoEs) are independent entities, wholly owned by the
municipality, mandated to perform municipal services and generate revenue for
local communities on behalf of a municipality (Khumalo, Ntlokonkulu & Rapoo
2003: 22). According to information contained on the Department of Defence
website, MoEs came into effect on 1 July 2004 under the Municipal Finance
Management Act 1 of 1999 (MFMA). This act was amended on 1 August 2004
and became the Municipal Systems Act (MSA) and the passing of the MFMA Act.
According to reports from the City of Tshwane (2019), the objective of establishing MoEs is to deliver basic services to the community, such as water and
electricity, waste management, recreational facilities, repairing of roads on behalf
of the municipality, and to conduct activities, which boost economic development.
Even though MoEs are established to fulfil the mandate of a municipality with
regard to service delivery and public service management, their operation and
governance remain autonomous. Their autonomy does not suggest privatisation.
Instead, it allows for independence and flexibility in governance, management
and operations.
1
More importantly, the contribution by MoEs cannot be limited to service delivery
only; instead, they play a crucial strategic role in economic growth, job creation
and poverty reduction in the country. According to the Municipal Finance
Management Act 1 of 1999 (MFMA) as amended in 2004, the formulation of the
autonomic strategies of MoEs is premeditated to give management the leverage
to take decisions without government interference.
It is noteworthy that within most municipalities, particularly in Metropolitan
Municipalities, services have to be decentralised through the establishment of
MoEs owing to the influx of inhabitants. The decentralisation of services, such as
the provision of water and electricity, waste management, economic development, recreational facilities, and the maintenance of roads forces the municipality to make proper analyses and respond positively to concerns raised by its
citizens. According to Klaus & Bahrinipour (2008: 25), the insufficiency of state
financial services requires the public sector’s participation to a point that it
complements the role played by the government to contribute to the provision of
service delivery. In fact, Crous (2002: 73) noted that service delivery is one of the
most important components of government and public administration.
According to the White Paper (1997) on Transforming Service Delivery, “public and
service delivery is not a privilege in a civilised and democratic society, particularly in
South Africa”. On the contrary, Riekert (2001:89), argues that, society is entitled to
demand the efficiency and cost-effectiveness of such services. Therefore, public
institutions must seek to provide the services needed by the community as customers.
This is particularly the case in South Africa, as the Apartheid inequalities evident in
infrastructure backlogs in poor black communities continue to persist. In support of this,
Mbeki (2000: 15) once observed, “many communities in the Republic of South Africa are
still challenged by the inheritance of under-development and poor service delivery
challenges despite strides made to redress backlogs. In fact, many people remain with
inadequate infrastructure” (Mbeki, 2005). Such a phenomenon remains challenging and
thorny for the South African government to address. Hence, the South African
government has developed a plan in its transformation priorities to address issues
regarding service delivery to cover the basic needs of communities and correct the
inequalities and injustices of the past (Republic of South Africa White Paper, 1997: 91).
2
1.2.1 The structure of debt financing for MoEs
The structure of debt financing for an entity or company consists of a mixture of
public and private debt financing (Damodaran, 2009: 4). Within the context of
municipalities and its entities, public debt financing refers to municipal grants,
stocks and other securities while private financing consists of debt and equity
financing (Sebapadi, 2016: 4). Even though the formation of MoEs is in line with
the Companies Act, MoEs do not sell shares. In that regard equity, shares and
debentures fall outside the ambit of this research. Kasozi (2009: 3) points out “the
question of how much debt an entity should have comparative to equity has
important implications for its value or its cost of capital”. The White Paper 1997
on Local Government recommends that municipalities should seek additional
private funding to supplement public funds. As a result, MoEs are not an
exception especially in circumstances where municipalities appear to be
financially constrained and are unable to fund MoEs because of poor financial
management. Section 13 of the Local Government Municipal Finance Management Act 56 of 2003 “makes a provision that a municipality may give assurance
of payment of the debt for its entity under its sole control, provided the guarantee
is approved by municipal council and depends on the similar conditions
appropriate to the municipality”.
MoEs are less constrained by the bureaucratic process of the shareholder
(municipality), and so their operations and performance may improve if they
follow the private sector service and business model. Conversely, and of particular interest, there is a need to identify factors that influence debt financing and
how they affect the ability of MoEs to meet their constitutional mandate. Sebapadi
(2016: 3) argues that both government funds, namely, inter-government funds
and the generated funds of municipalities are usually scarce or limited. As a
result, borrowing becomes an important tool to source additional funds,
particularly in developing countries like South Africa (Sebapadi, 2016). To
corroborate this argument, Brown et al. (2014: 532) stated that “national
government’s prevailing sources of capital finance, namely, municipalities’
internally generated funds and intergovernmental grants are inadequate to meet
the projected demand” required by MoEs. According to the authors, therefore, the
use of debt appears to be an alternative way of raising additional capital.
3
Debt capital refers to loans from shareholders, debentures, other long and shortterm bank loans as well municipal bonds. Hovakimian et al. (2003: 518) posit that
both debt and equity capital are sources of financing which are used by entities
to finance operations and investment activities. As mentioned in earlier sections
of this chapter, however, South African MoEs do not have equity financing.
Because of this, the study will be limited to debt finance.
According to Cassar (2004: 261), financing decisions, particularly decisions
pertaining to debt financing, can be seen to contain remarkable implications for
the entity’s operations, risk of adversity, performance of the attributes of the
entity, and the prospect of the entity to expand. Brown and Motsoane (2014)
noted how the mainstay lending of municipality had been commercial loans. The
authors, however, noted that, lately, bond markers appear to be more popular
and have become a common source of funding within MoEs (Brown & Motsoane,
2014: 518). Although there has been a lot of empirical evidence presented before
by authors such as Andrus and Jones (2002), Mansi and Reeb (2002), Frank and
Goyal (2009), and Nyamita (2014), amongst others, on the various factors that
influence debt finance, the focus of these authors has been on private entities.
Interestingly, there is a paucity of literature relative to factors that influence debt
financing in South Africa, particularly for MoEs. Given this phenomenon,
therefore, the study seeks to close this gap. This research focuses on the debt
financing of MoEs in the CoJ and the Republic of South Africa by acquiring an
understanding about the policy on debt finance and demonstrating the
importance of debt financing to MoEs. Moreover, the study has also determined
the extent of debt financing and the debt instruments of MoEs in the Republic of
South Africa, using the CoJ as a case study.
Table 1.1 below lists the 13 MoEs that fall within the CoJ and the core services
they render to citizens of the City. The City wholly owns all the MoEs. They play
a critical role in the advancement of economic growth, water and electricity, waste
management, economic development, recreational facilities, job creation, poverty
reduction, and the maintenance of roads
4
Table 1.1: City of Johannesburg municipal-owned entities database
Name of the MoE
Core service renderers
City Power
Distributes electricity bought from bulk generators.
Johannesburg City Parks
Manages the City’s graveyards, parks and certain public open
spaces. It also safeguards the environment, including the
maintenance of all street and parks within the CoJ’s borders.
Provides access and conservation projects for indigenous and
worldwide-endangered animals and supports the conservation of
wild populations and habitats.
Johannesburg City
Theatre
Stages both Broadway musicals and homegrown productions.
Johannesburg
Development Agency
Contributes to the growing the competitive business
environments of the city, improving public spaces and promoting
access to jobs.
Johannesburg Fresh
Produce Market
Produces and provides fresh fruit and vegetables to the city.
Johannesburg Property
Company
Oversees property development to the whole of the greater CoJ.
It also provides property management services and all ancillary
services.
Johannesburg Social
Housing Company
Provides quality affordable housing to the citizens of
Johannesburg.
Johannesburg Road
Agency
Oversees the repairs, maintenance, and development of CoJ
roads network and storm water infrastructure, including bridges
and culverts, traffic lights and signage.
Johannesburg Tourism
Company
Is responsible for stimulating tourism growth through demanddriven tourism development.
Johannesburg Water
Provides all people of Johannesburg with access to quality water
and sanitation services.
Johannesburg Metrobus
Offers a consumer-friendly public transport within greater CoJ in
the form of buses.
Metropolitan Trading
Company
Provides excellence of market and transport interchanges in
Johannesburg.
Pikitup
Oversees the cleaning of the CoJ, to ensure that there is an
attractive and hygienic environment for all inhabitants.
Source: Self-generated by researcher, 2018
5
1.2.2 The role and relevancy of debt capital structure
Modigliani and Miller (1958) introduced the theory relative to capital structure
under the title of the “The cost of capital, corporation finance and the theory of
investment”. Precisely, the authors specified that, based on certain assumptions,
capital structure decisions are irrelevant. If one, thus, takes into account the work
of Modigliani and Miller (1958), whether an entity is highly leveraged or has a
lower debt component in the capital structure it would not have any bearing on
the value of the entity. Despite sparking a lot of interest among scholars, the work
of Modigliani and Miller (1958) attracted a number of critics. For instance, Stulz
(2000: 121) criticised their work and argued that, although the vital assumption of
Modigliani and Miller (1958) frequently holds, they allow only for mere predictions
to be made. As a result, the outcomes should be adjudicated by leading
assumptions and by the expediency of the results in explaining the empirical
phenomena.
There have been many empirical studies, which have examined the relationship
between debt capital and performance. For instance, Otieno (2015: 1) argues
that an understanding of the association between performance and debt capital
enables the validation of the part which debt capital plays in the corporate
governance of entities. Moreover, it is uncertain whether the entity’s performance
guides capital structure decisions or whether performance is propelled by capital
structure or whether both act in tandem. The strength and growth are some of
the key measures of the entity’s performance, and all stakeholders have an
interest in the level of the entity’s financial performance. Consequently, the
strategy of the entity should include debt financing in order to improve financial
performance (rate of return on the owner’s investment) by producing a higher
return on funds borrowed (Nyamita, 2014: 6). Otieno (2015: 51), however, argues
that, in order to select an appropriate measure of performance, there should be
an examination of bi-directional association between performance and capital
structure. The study by Luscombe (2009: 16) proved that profitable firms tend to
use less debt finance, and this was supported by Denisa and Mihovb (2002: 21)
and Chenesai (2009: 12). It is, thus, predictable that profit-generating entities use
less debt to shield the entities from liquidation.
6
Other empirical studies identified factors that influence debt financing in the private
sector, especially in developed economies (Hackbarth, 2009: 507; Antoniou, Guney
& Paudyal, 2008: 59). Profitability, corporation size, growth, industry conditions, tax
rates, stock market conditions, business risk, capital markets and the nature of the
assets are some of the factors observed to influence debt financing. Frank and Goyal
(2009: 1), however, argue that the conclusion regarding factors influencing debt
finance remain vague despite this being a well-researched topic. It is interesting to
note how limited the studies on debt financing are in developing economies like
South Africa, with a few studies undertaken by authors like Sibindi (2017) and Malaza
(2017). Nyamita (2014) conducted a study on this topic based on SoEs operating in
Kenya. The author argued that there is no agreement concerning the empirical
conclusions on factors influencing debt financing in entities. Against this background,
the purpose of this study is to examine factors that influence debt financing of MoEs
for the CoJ. The results of the study will contribute to filling the existing gap in the
literature on debt financing of MoEs. Furthermore, the findings of the study will
contribute to capital structure decisions for MoEs in South Africa in general and for
the CoJ in particular.
1.3
PROBLEM STATEMENT
The theory on the choice of capital structure, particularly debt, has been given much
attention over the past decades, both in public and private entities especially in
developed countries. The approach towards the borrowing of MoEs is generally from
within the framework of municipal borrowings under section 87 (5) (d) of the MFMA.
This limits the entities from exercising a flexible borrowing approach (MFMA, No 56
of 2003). According to Sebapadi (2016: 4), it is difficult for a municipality and its
entities to live up to their set mandate because of the lack of significant requisite
knowledge of the investment and risk of debt instruments used by municipalities and
MoEs for capital funding. He further emphasises that, currently, bank credits or loans
and municipal bonds are the two main financing instruments of sub-national
borrowing in South Africa. The bank credits in the form of short and long-term loans
acquired by MoEs bear interest. Ward and Price (2008: 57) assert that all interestbearing borrowings should be considered if you want to evaluate the entity’s
performance regardless of the type of the debt held, whether short or long term.
Jooster (2015: 6), however, argues that the use of more debt by an entity in the
capital structure to finance operations and investments often creates financial risk.
7
The applicable legislation in line with borrowing choices is set in Act 56 of the Local
Government Municipal Finance Management of 2003. It is noteworthy that the lack
of a flexible process in accessing debt financing can be viewed as being one of the
most significant impediments for municipal entities. This is a serious constraint which
show different effects and responses to credit availability for municipal entities,
because the impact on debt financing is directly associated with the parent
Municipality. This motivated the undertaking of the present study. Although
researchers, like Sibindi (2017) and Malaza (2017) have already investigated the
concept of debt financing in South Africa, their area of focus has been on private
entities. On the other hand, Nyamita (2014) conducted a similar study on debt
financing but based it on state-owned corporations in Kenya. In the South African
context, Sebapadi (2016) paid particular attention to the parent municipalities in
South Africa instead of focusing on the entities owned by these parent municipalities.
Given the above findings and arguments, the study ought to find out whether the
outcomes of such past studies present a similar view for municipal entities and as
well as closing the gap using data from the City of Johannesburg.
Klaus & Bahrinipour (2008: 6) posits that, currently, bonds are no longer economic
compared to loans because interest rates are very low owing to serious competition
and insolvency in the Banking Sector. From the above arguments, it is quite clear
that advanced and emerging economies have adopted banking credits, and entities
prefer loans because the interest payment on loans is tax deductible that is beneficial
to borrowers. In addition to the above regarding interest-bearing loans, Deesomsak
et al. (2004: 395) noted how factors such as taxation and bankruptcy costs and
changes in interest rate could influence the debt financing level within an entity. The
authors attributed this to the fact that most entities are more likely to use debt
financing when the cost of debt is low. In fact, in their study, Deesomsak et al. (2004)
also concluded that taxation has an impact on corporate financing.
Given the above, this study also ought to find out whether the outcomes from studies
such as those carried out by Deesomsak et al. (2004) and Nyamita (2014) provide
similar results especially for municipal entities in the City of Johannesburg, South
Africa. Precisely, the study set out to establish whether there is a relationship
between debt finance and factors such as interest rate, profitability, size, corporate
tax, age, business risk, GDP, tangible assets, economic growth and entity growth in
the City of Johannesburg.
8
Secondly, the researcher sought to establish the type of debt instruments and
extent of debt financing substantively preferred by City of Johannesburg.
1.4
RESEARCH QUESTIONS
The main aim of the study is to investigate factors that influence debt financing of
MoEs for the CoJ for the period 2011–2015. The investigation has explored and
answered the following pertinent questions:
The primary research question to be answered is:
•
What are the factors that influencing debt financing within MoEs in the
CoJ?
In addition to the above primary research question, the study will respond to the
following secondary sub-questions:
•
Which different types of debt financing do MoEs in the CoJ use?
•
What extent of debt financing does MoEs of the CoJ substantively prefer?
•
What are acceptable levels of debt financing in the MoEs for the CoJ?
1.5
RESEARCH OBJECTIVES
The research objectives of the study are as follows;.
The primary research objective is;
•
To identify the factors that influence debt financing of MoEs within CoJ;
The secondary sub-objectives are as follows:
•
To analyse the different types of debt used by the various MoEs for the
CoJ;
•
To determine the extent of debt substantively preferred by MoEs in the
CoJ;
•
To establish the acceptable level of debt for MoEs for CoJ.
9
1.6
RESEARCH HYPOTHESES
Creswell (1994) describes hypothesis as a formal statement that presents the
desired outcome between an independent and dependent variable. In addition, a
hypothesis is a research statement that is still to be accepted or disapproved
(Helmenstine, 2017).
In order to test whether the independent variables have an influence on debt
financing, different types of hypotheses were set for each objective in the study.
To answer the research objectives listed in section 1.4, the following hypotheses
were formulated.
•
To determine whether a relationship exists between size and debt finance,
the relevant hypothesis was formulated as follows:
H1: Size of the entity influences debt positively.
•
To determine whether the age of a firm has a relationship with debt finance,
Hypothesis 02 was formulated as follows:
H2: Age of the entity influences debt positively.
•
In the same vein, to determine whether there is an association between
profitability and debt finance hypothesis 03 was formulated:
H3: Profitability of the entity influences debt negatively.
•
The other factors that were investigated were business risk, corporate tax,
asset tangibility, growth, inflation rate, interest rate, GDP, and economic
growth. The relevant hypothesis statements were formulated below as follows:
H4: Business risk influences debt negatively;
H5: Corporate tax influences debt negatively;
H6: Assets tangibility influences debt positively;
H7: The growth of the entity influences debt negatively;
H8: Inflation rate influences debt negatively;
H9: Interest rate influences debt negatively;
H: Gross domestic product influences debt positively; and
H11: Economic growth influences debt positively.
10
1.7
SIGNIFICANCE OF THE STUDY
While MoEs play a critical role in providing basic services to inhabitants of
municipalities, they are required to be self-sustainable and contribute to the
generation of third-stream income of municipalities. Given this problem, factors
influencing MoEs debt financing in developing countries is crucial and warrants
empirical attention. The study attempts to make significant contributions in a
number of areas particularly to the empirical literature on the factors that influence
the debt financing of MoEs in South Africa. In addition, the study will contribute
to the growing body of research on capital structure particularly in the public
sector. Finally, the study can be a valuable source of information for senior
finance people in decision-making. It is equally important to note that there has
been relatively little literature published on what drives MoEs to opt for debt
capital.
1.8
RESEARCH METHODOLOGY
According to Chenesai (2009), there are three types of research methods one
could choose from when conducting research. These are quantitative, qualitative
and mixed method (Chenesai, 2004). In this study, however, the quantitative
method was employed. Chenesai (2009: 95) defines quantitative research as “a
method that derives empirical generalisations, which may be used to determine
future courses of action”. Madrigal and McClain (2012) also noted that the
greatest strength of quantitative method is that it provides data that is descriptive
in nature, so permitting a researcher to capture a picture of a user population. It
has, however, been observed that the greatest weakness of the quantitative
research method lies in the interpretation thereof (Madrigal & McClain, 2012).
As indicated before, apart from quantitative methods, there are other research
methods namely, qualitative and mixed methods. According to Dowd (2004: 204),
“qualitative research is the utmost effective technique to conduct empirical inquiries
meant for better understanding phenomena occurring in their natural context”. The
author also described mixed method, as “a procedure that frequently includes the
use of both qualitative and/or quantitative methods”.
11
For instance, a mixed method study may involve the use of qualitative methods
to describe some experience, while employing quantitative methods to measure
some dimension of the experience. It worth noting that, for this study, a quantitative method was applied. A diagnostics test will be carried out to determine the
appropriateness of the model and the results will be presented in Chapter 5.
In addition to ensuring that a study uses a certain research method, it is equally
important to indicate that the study has relied on a particular research paradigm.
According to Saunders et al. (2012: 140), there are four research paradigms, a
scientific approach and tested hypothesis to determine how independent variables influence debt financing, making it positivistic in nature.
1.8.1 Research design
According to Tustin et al. (2005: 82), “research design is a plan to be followed to
realise the research objectives or hypothesis.” In fact, according to the authors,
the research design provides the master plan, which determines ”the approach
and procedure for gathering and scrutinising mandatory information”. A mixture
of descriptive and correlational research design models were used to estimate
the relationships amongst the variables under study. These were then analysed
using Eviews. The applied techniques enabled the researcher to investigate the
types of debt used, the extent of debt substantively preferred as well as the trends
of the acceptable debt level for a period of 5 years from 2011–2015 within the
MoEs for the CoJ. The scope was limited to five years due to the lack of
availability of correct post-2015 records for some MoEs. The data used in this
study were obtained from integrated annual reports and annual financial
statements.
1.8.2 Target population
The target population of the study consisted of the MoEs within the CoJ in the
Gauteng Province. The researcher chose MoEs from the CoJ because it has the
majority of MoEs in the Gauteng Province. The total number of MoEs in Gauteng
is 18, and 13 of those belong to CoJ while the rest are spread between the two,
City of Tshwane two and three for Ekurhuleni.
12
1.8.3 Sampling design
There are five non-probability sampling methods, namely judgemental, convenience, purposive, quota and multiplicity sampling. For the purpose of this
study, however, ‘convenience sampling’ methods were used. To be precise, the
sample selected consisted of all 13 MoEs from the CoJ. This was because there
were not enough MoEs from which to sample. Moreover, the reason for selecting
all municipal entities that fall within the City of Johannesburg was that their annual
financial statements are easily and readily available online. In the event that some
annual financial statements for a certain period were not made available, the
entity was contacted and requested to make the information available to the
researcher.
1.8.4 Measuring instrument
The information from the integrated annual reports and financial statements was
used to measure variables across the MoEs. The researcher used financial ratios
to measure the variables under study. The variable for debt financing was
measured as X while the independent variables for interest rate, profitability, size,
tax, age and business risk were measured respectively. These variables were
extracted from the annual financial statements of the MoEs from 2011 to 2015.
In the event that financial statements were not clear and specific, further clarity
was sourced from the annual reports, which were readily available on the official
MoE’s website.
1.8.5 Data collection
According to Tustin et al. (2005: 88), there are two types of data source that a
researcher can use to undertake a study; these are primary and secondary data.
1.8.5.1 Primary data
The use of primary data was not applicable for this study because the researcher
had assessed its value and discovered it to be inadequate for the research
objective. This was due to a poor response from the lime survey sent to potential
respondents, which resulted in a 13% response.
13
1.8.5.2 Secondary data
Various copies of annual financial statements were used as a source of secondary data for longitudinal data analysis. Where this information was unavailable or
unclear, further information was obtained from the office of CFOs, senior.
1.8.6 Data analysis
The following data analysis methods will be used to determine the above-mentioned objectives:
•
Descriptive statistics were used to determine the extent of debt substantively
preferred and to compare the different types of debt used by MoEs;
•
The Panel EGLS (Period SUR)regression model was used to determine the
factors that have an association with debt financing; and
•
A statistical package called Eviews was used to analyse data. The results are
presented in Chapter 5 of this study.
1.8.7 Anonymity and confidentiality
As indicated earlier, data was collected from the entities’ annual financial statement as well as from the integrated annual reports. Apart from the information
obtained from the annual financial statements, the identity of participants from the
lime questionnaire will be kept confidential, though such information had no role
to play in the study. The researcher, thus, upholds the anonymity of the
participants who assisted with some information not made available and accessible in the public domain in compliance with the POPI Act. According to the
Act, it is essential for a researcher to protect the personal information by public
and private bodies. Moreover, the Act mandates that all South African institutions
that gather information should maintain the confidentiality of respondents when
gathering, processing, storing, and sharing another entity's personal information.
In fact, according to the Act, researching bodies and institutions should be held
responsible should they compromise or abuse personal information. After
concluding this study, the data collected will be retained for 15 years for any
further analysis and then disposed of.
14
1.8.8 Validity and reliability
The Arellano-Bond estimator and Robustness will be used to test for the validity
and reliability of the study regression models and variables. Koenker (2007: 214)
describes robustness in statistics “as a system, which has an ability to signify
resilience of conclusion to deviations from assumptions of hypothetical models”.
1.9
ETHICAL CONSIDERATIONS
The study was conducted strictly according to University of South Africa’s
Research and Ethics Policy. An ethical clearance was obtained from UNISA’s
College of Accounting Sciences Research Ethics Committee. A formal letter was
sent to different MoEs requesting permission to request information that was not
made available for a particular period. The POPI Act kept the identities of the
respondents who assisted with the information that was not publicly available
private and confidential by using pseudonyms and as set.
1.10
DELIMITATION AND LIMITATIONS
1.10.1 Delimitation
The study covers MoEs of the CoJ in Gauteng, using non-probability sampling
techniques. The sample size for the study consisted of 13 revenue and profit
generating MoEs. Annual financial statements and integrated annual reports
were used to measure the validity and reliability of the research. Financial
leverage variables, such as Total debt financing (TD), long-term debt (LD) and
short-term debt (SD) were used to measure debt financing levels (financial
leverage) between 2011 and 2015. Because the MoEs are scattered around
Johannesburg, the researcher visited each entity only to collect copies of the
financial statements from the CFOs, Financial Managers and Senior Managers
in an event where the information was not available. The study used secondary
data as the main source of data.
15
1.10.2 Limitations
The initial main objective of the study was to determine and establish factors that
influence the debt financing of MoEs. Despite the strength of the methodlogies
used in the study there are, however, still some limitations. As mentioned earlier,
one measuring instrument, the annual financial statements, was used for the
validation and reliability of data as per the content and predictability of the
research. It worth noting that primary data could not be used for this study, owing
to poor responses from the lime questionnaire sent to 60 prospective participants.
The lime questionnaires consisted of 12 questions and such a limitation could not
be avoided. The average response rate was 13%. The 13% non-response rate
was viewed as a limitation of this study. Even though the responses might have
been positive, primary data was not the main source of data for the study. It,
however, sought to complement and clarify any information obtained from
secondary data sources.
1.11
DEFINITIONS OF TERMS AND CONCEPTS
1.11.1 Municipal-owned entity (MoE)
In terms of the Public Finance Management Act 1 of 1999, a municipal-owned entity
is an independent municipal entity, wholly owned by the municipality. It is mandated
to perform municipal services and generate revenue for local communities on behalf
of a municipality, is established and registered in terms of the Company Act of 2008,
is listed or not as a public entity in Schedule 2 or 3 of the Public Finance Management
Act 1 of 1999. As described by the Community-Wealth Organization, a MoE is an
independent entity, wholly owned by the municipality, mandated by the municipality
to perform services and generate revenue for local communities on behalf of a
municipality. Municipally-owned entities and state-owned entities are legally formed
with a common fundamental principle, which is to render public services. Unlike a
MoE, a state-owned entity (SOE) has an exception in terms of ownership. Capalbo
and Palumbo (2013: 39) point out that MoEs and SOEs can be owned partially or
wholly by national government or government, or even by an agency controlled by
them. Sadiki (2015: 42) expressed the view that the role to be played by MoEs and
SOEs is significant in advancing economic growth and that, therefore, their mandate
is to effect government priorities of developing important infrastructure and manufacturing capacity for South Africa.
16
1.11.2 Capital structure
Modigliani and Miller (1958: 453) define ‘capital structure’ as a long-term source for
funding used by a firm consisting of a mixture of both debt and equity financing.
Myers (2001: 81) states that capital structure attempts to clarify the mixture of
securities and sources financing used by entities to finance real investment.
1.11.3 Debt financing
Denis and Mihov (2003: 4) describe debt financing as the major new source of
external funding in the US entities. In the context of MoEs, debt financing consists of
bonds, short and long-term loans. Debt is an amount owed to a third party for funds
borrowed or amount to be paid to third party for services rendered or sales of items
and its common practice within SOEs (Nyamita, 2014: 51).
1.11.4 Financing
Financing is an act or a process of raising capital for an entity in order to finance
operational costs or investments (Bassey, Arene & Okpukpara, 2014: 36 in Nyamita,
2014: 15).
1.11.5 Financial leverage
Financial leverage is described as a reflection of the capacity of financial managers
to entice external financial resources in order to improve the efficiency total debt of
the firm (Kzistami 2011: 1).
1.11.6 Profitability
Profitability is the capability of an entity to earn a profit. According to Modigliani and
Miller (1963: 24), firms with high profits should employ high debt in order to benefit
from the interest tax shield. Chenesai (2009: 11) asserts that the decision to use
external equity could also affected by profitability.
1.12
THESIS STATEMENT
A MoE is an independent municipal entity, wholly owned by the municipality,
mandated to perform municipal services and generate revenue for local communities
on behalf of a municipality.
17
Even though a municipality owns them, MoEs are run independently and they
generate their own income apart from the municipal subsidy. The management of
MoEs, therefore, has the right to determine the capital structure of their entities
without their being dictated to by the municipality but they should comply with the
policy.
The study of capital structure, particularly debt, has been enjoying much attention
over the years. Even though there has been empirical evidence presented from
different studies, there seem to be no consensus regarding the topic. In South Africa,
there have been a limited number of studies on debt financing, particularly with
regard to MoEs. Based on the available literature, debt has been impacted by various
factors in entities. Different studies have identified common factors like economic
condition, politics, and inflation rate.
Based on the gap in literature on debt financing, the study has formulated and
presented evidence as well as empirical tests on specific factors that influence debt
financing. Given the fact that there are limited studies on this topic, particularly in a
developing country like South Africa, the study contributes to the body of knowledge
in the South African MoE context. To achieve this, a descriptive design to develop
and present empirical evidence on the problem under study wad been created. This
was an incomplete sentence and made no sense. I hope it now says what you wished
it to say.
1.13
OUTLINE OF THE STUDY
Chapter summary
Chapter 1 provided a thorough orientation to, as well as background of, the study. It
further outlined the problem statement, which is the foundation and the pillar of the
study. In addition, the chapter discussed a few key theories and empirical studies to
contextualise the problem. From there, methodological issues, ethics and the study’s
limitations were addressed. A summary of the significance of the study and brief
definitions of key terms were given. The next chapter focuses on the theories
regarding capital structure and local government finance. Chapter 3, which provides
a detailed analysis of empirical literature, follows this. Chapter 4 is an in-depth
discussion of the methodology used to achieve the objectives of the study. This is
followed by a data analysis in Chapter 5 and conclusions and recommendations in
Chapter 6.
18
CHAPTER 2
THEORETICAL FRAMEWORK
2.1
INTRODUCTION
The previous chapter attempted to cover the set objectives of the study. The aim
of this chapter is to present the theoretical framework on the factors influencing
debt financing of municipal-owned entities (MoEs). The chapter presents
important insights into the theoretical concept related to Capital Structure, specifically on debt financing. The purpose of adopting these theories in this study
has been to describe how the debt financing model is adopted and explain the
implementation of different aspects associated with debt financing within the
MoEs. Secondly, the theory addresses the key theoretical concepts on Capital
Structure and debt financing in relation to local government as encapsulated in
the Public Financial Management Act (PFMA) 1 of 1999 and Municipal Finance
Management Act 56 (MFMA) of 2003 and section 46 of the Municipal Systems
Act (MSA) of 2000. A related concept considered is the definition of debt financing
and the financing model used by municipal entities in South Africa. In conclusion,
the use of economic theories of regulation for public entities is fundamental and
vital. It is, however, noteworthy to mention that the current study falls within the
ambit of public administration management.
2.2
CAPITAL STRUCTURE
Modigliani and Miller (1958) conceptualised the theory on Capital Structure. Since
then, various definitions and principles regarding the concept have been developed.
A typical example is that of Luscombe (2009: 9), who defined a Capital Structure “as
the equilibrium between equity and debt that is used for financing operations and
new projects”. Subsequently, the theory about Capital Structure postulates that
management should point out the mixture of Capital Structure that will increase both
the wealth of the shareholder and the market value of the entity (Otieno, 2015: 4).
Modigliani and Miller (1958) introduced the concept of the Capital Structure theory
under the theme “Irrelevance proportion of Capital Structure.”
19
Their classic analysis of irrelevance proportion of Capital Structure based the
value for the entity on the idea that cash flow is unaffected by Capital Structure.
Furthermore, Myers (2001: 81) supports this view by pointing out that an entity
seeks debt financing that balances the tax advantage of additional debt against
the possibility of bankruptcy costs. There is a considerable amount of literature
published by earlier scholars and, to date, on Capital Structure (Robichek &
Myers, 1966; Borch, 1969; Jensen & Meckling, 1976; Miller, 1977; Schneller,
1980), among others. The majority of earlier studies on Capital Structure were on
developed and developing countries. There are, however, few studies on debt
financing within South African entities, especially in MoEs.
2.3
LOCAL GOVERNMENT
The government of South African is separated into three scopes of systems,
namely the judiciary, Parliament and the executive. The role of government
universally is to provide services and implement policy objectives for its people.
The Constitution is the supreme law that governs South Africa, and it makes
provision for three constituencies to govern the states, namely national, provincial
and local government. Furthermore, the Constitution of South Africa 1996, under
section 151(1), provides local government with the legal right to establish
municipalities for the entire territory of the Republic. A municipality has the
powers to govern, in its own initiative, the affairs of its local government within its
community provided by provincial and national legislation. The provincial and
national government might not, however, compromise or obstruct the capacity or
right to perform their functions and exercise their powers of municipalities
(Sebapani, 2016: 10).
In general, local government falls within Department of Cooperative Governance
in the Ministry of Cooperative Governance and Traditional Affairs (CoGTA)
(2008). According to Greÿling (2014: 3), section 72 (1) of the Local Government:
Municipal Systems Act 32 of 2000 (hereafter also interchangeably regarded as
the Municipal Systems Act of 2000) indicates that the Minister for CoGTA makes
regulations on capacity building in municipality administration.
20
According to the South African Yearbook (2013: 299), earlier in 2000, local
government went through a demarcation transitional phase, which reduced the
size of municipalities from 843 to 278, comprising eight metropolitan
municipalities, 226 local municipalities and 44 districts. According to Lee Gardyne
(2005: 63), the demarcation process gives local government some elements of
independence in decision-making and greater freedom in carrying out policy,
which enables municipalities to evolve disparities within the structural scheme.
In the same vein, the establishment of municipalities has its own challenges, and
one of the crises facing municipalities is service delivery. This phenomenon has
had a direct impact upon the lives of ordinary people within the community.
According to Dau (2010: 19), a report by South African Municipal Workers Union
(SAMWU, 2009) noted with disappointment the increase in corruption in the
provision of basic services and awarding of tenders, disregarding the need for an
improved standard of living of the poor communities who have been waiting
patiently for quality service delivery.
In dealing with above, most municipalities have decentralised or outsourced their
services by establishing MoEs. These entities provide basic services to the
people on behalf of the municipalities. Such an outsourcing model is among the
qualities that anchor an effective and good public service and financial management practice. In terms of public services, the Financial Management Act 29 of
1999 seeks to ensure that there is good governance, accountability, assets, liability, sound revenue management and expenditure, and such phenomena instil
discipline in the public sector.
2.4
PUBLIC FINANCIAL MANAGEMENT ACT
The other significant legislative act passed by the first democratic government of
South African was the Public Finance Management Act (PFMA) 1 of 1999 (as
amended). According to Sadiki (2015: 58), the Act attempts to inspire the objecttives of decent financial management in order to increase the delivery of
services over the effective and efficient use of restricted resources of public
entities including MoEs.
21
Klaus & Bahrinipour (2008: 57) report that such a governing framework must not
forbid sub-national borrowing but relatively enable sub-national entities to engage
financial markets independently to fund their projects. MoEs are formed within
the parameters of Company Act, the PFMA 1 of 1999 (as amended) and the
Municipal Financial Management Act (MFMA) 56 of 2003, which guide their
financial management principles. Among others, the important objectives within
the PFMA are to:
•
Eliminate the corruption and waste of public assets usage;
•
Allow managers of public sector to manage, but in the same breath, be held
accountable;
•
Standardise the system of financial management in the public sector; and
•
Ensure that the dissemination of quality information must be timely.
One of the fundamental pieces of the legislation that accounting officers or
financial managers must comply with is the PFMA. Moagi (2009: 2) argues that
sets of regulations in the Act are not applicable only to the accounting officer or
the financial manager. Instead, all public servants should be familiar with the Act
and should ensure that their conduct is within the limitations of the PFMA.
Christensen and Laegreid (2013: 106) argue that after the re-organisation of
financial administration in many governments, under the new public management, public financial management has followed three main refrains, namely (1)
the use of market-oriented mechanism, (2) introducing an accrual-based management accounting system and (3) promoting result-oriented management
(Nyamita et al., 2015: 25). The executive managers and financial managers need
to be qualified in order to implement the principles of PFMA effectively and
adequately.
The financial management system of financial management within the public
sector is streamlined by the PFMA in order to encourage the objectives good
financial management in order to enhance delivery through the effective and
active use of resources (Moagi, 2009: 2). Even though municipalities have
granted all the autonomy and responsibilities to their entities, MoEs may not
deviate from the PFMA and its functions, but they are relieved from political
bureaucracy and permitted to be more flexible.
22
According to Erasmus (2002: 9) and McKinney (2004: 2), public financial
management comprises the following functions, budgeting, accounting, financial
management systems, control and performance management, expenditure
management and public accountability. Above all, the National Treasury remains
the arbiter with regard to overseeing and imposing standards and norms, and it
further plays an oversight role relative to the finances of other organs of State in
all areas of the South Africa government (Moagi, 2009: 2).
2.4.1 Public financial management functions
The principal function of public financial management is applicable to all government units, local government or any state or municipally owned entity (Nyamita,
2014: 26). Given the above circumstances, a need for a more efficient and effective public financial management function will drive a smooth operation of the
entity. These include budgeting, financing, expenditure management, financial
reporting, auditing, and accountability.
These generic functions govern the operations of MoEs and are discussed below.
•
Budgeting
According to Jordaan (2013: 14), a budget is a quantified, planned course of
action over a conclusive time and period. It is an attempt to forecast inputs
and the prices of inputs along with related outputs and revenues from outputs.
In addition, Visser and Erasmus (2002: 9) define budget as a framework that
links the spending objectives with their related costs. According to
Modernising Financial Governance Implementing the Municipal Management
Act, 2003, National Treasury, the MFMA, No 56 of 2003, National Treasury,
budgets accommodate demands that are immediate with minor or no options
relative to future needs or the forthcoming consequences of specific decisions.
Fiscal and public taxes budgets are not sufficient to bridge the infrastructure
gap, coupled with the South African government’s lacklustre record of
accomplishment regarding spending on infrastructure (Sebapadi, 2016: 8).
23
•
Financing
According to Matanga (2013: 18), financing could be done in two ways. The
first financing form could be in the form of long-term debt in terms of preference shares and common equity, which are used to provide capital to
enhance business growth through investors. The investors in return receive
a return on investment payments for debt holders, while in the context of
common equity preference dividends and dividends are payable. The second
financing could be in a form of short-term financing, which includes working
capital and trade financing. The working capital is regarded as the current
assets that an entity has in the form of inventories. Cash and receivables are
used to finance the short-term debt obligations such as overdrafts, short-term
loans and account payables (Matanga, 2013: 18). Klaus & Bahrinipour (2008:
43), however, argue that it does not astonish that debt financing assists in
funding the growth-enhancement infrastructure and signs that debt financing
assists in the reduction of a backlog by providing basic services are the
critical results.
•
Expenditure management
Visser and Erasmus (2002: 9) describe expenditure management activities
that involve the actual day-to-day spending of money related to the unit’s
approved budgets and the day-to-day governmental unit’s operational
procedures linked to the execution of policy objectives stated in the budget.
Even though the expenditures are properly authorised within the framework
of the budget, this does not preclude the expenditure management unit carried out to yield good outcomes for money, people-focus and sustainability
(Tilley, 2014: 65). In the case of this study, MoEs expenditure management
should promote the sustainability and the enhancement of public services
delivery.
According to Jordaan (2013: 183), experts employed by the State make all
public expenditure decisions that seek to inspire a joint utility. In this case,
pro-poor policy/principles guide public expenditure. Liedo et al. (2005:31)
elaborates on the fact that public expenditure translates policy priorities into
the services delivery to societies and is, therefore, an important tool for accomplishing public goals.
24
The World Bank argues that Public Expenditure Management (PEM) tends to
promote the success of three outcomes, namely aggregate fiscal discipline,
allocative efficiency, and operational efficiency. The last refers to the provision
of public services at a reasonable quality and cost. The fiscal structure and
institutions of public-sector financial management were envisioned to provide
the decision-makers and ruling elite maximum control (Nyamita, 2014: 64).
Low tariffs charged by the services of MoEs are, however, not a signal for
operational efficiency.
While different definitions can be given; there is a common understanding in
the public discourse that defines expenditure management as a process of
managing the day-to-day spending, which is consistent with the first definition
discussed.
•
Financial reporting
The Financial Management Act (FMA) regulates the financial reporting of
MoEs. This Act requires clear and consistently formatted information about
the financial status of MoEs. According to Moloi (2008: 20), financial reporting
is designed to address the agency problem, so providing defence for the
company investors. Similarly, van Helden et al. (2010: 83) concur with the
view that suggests that financial reporting is likely to be more effective in the
public sector’s financial management. To enhance the effectiveness of the
public sector’s financial management, a monitoring mechanism should be
developed. Public financial reporting contributes significantly to the completion of public financial management objectives with internal and external
reporting for accountability purposes (Nyamita, 2014: 29).
In order to improve the monitoring of performance and financial reporting
requirements for all the public entities, there should be a revision by the public
sector to bring it nearer to the reporting of private sector entities (Nyamita,
2014: 29). With this in mind, the Municipality Management Act (MMA)
stipulates that certain reporting mechanisms must be established within the
MoE to monitor the financial position of the entity and assess continuous
progress towards performance objectives.
25
Risk management and internal controls play a significant role in upholding
the effectiveness and efficiency of operations to ensure reliability on the
financial reporting, compliance with law and regulations and the prevention
and detection of fraud. For this reason, the information relating to risk
management and internal controls is crucial (Moloi, 2008: 109). The
significance of the MFMA’s recommendation is that regular current reporting
will strengthen the effectiveness and efficiency of financial reporting.
Such an exercise recommended by the MFMA promotes financial management, accountability and transparency of the MoEs and the public services.
Jordaan (2013: 6) further justifies the point further that financial reporting is
an obligation that supports good corporate governance through outward and
internal reporting. Outward refers to reporting to Parliament and all external
stakeholders. AICPA (2012: 1843) cited in Otieno (2015: 58), however,
concludes that the measurement objective of accounting estimates can differ,
depending on the appropriate financial reporting framework and the financial
item reported.
•
Auditing
Auditing includes the process of inspection and evaluation of the financial
statements of an entity to ensure that there is a fair and accurate record of
financial transactions. Mostafayi (2012: 4) states that auditing is a systematic
and independent process that exists to determine whether the activities in
place and their results have adapted to planning issues and requirements
and whether they are appropriate for the effective achievement of objectives
and performance. According to Nyamita (2014: 30), both governmental scholars and practitioners recognise the importance of auditing and emphasise
that, through quality auditing, better organisational processes and results are
achieved. In the context of MoEs, public entities and SOEs, government
outsourced the function of audit to its own audit firm by the name of the
Auditor-General.
26
The Auditor-General South Africa (AGSA) is an extension of a government
audit entity given the full mandate and responsibility to conduct audit
processes for its entities. AGSA is a “Chapter 9” institution (sections 181 and
188) of the Constitution of the Republic of South Africa). Furthermore, AGSA
is the Supreme Audit Institution (SAI) of South Africa. It is the only institution
that, by law, has to audit and report on how Government spends the money
of the South African taxpayer. Such an audit process by AGSA is not only
limited to entities and government departments but cuts across all spheres of
executive and bureaucratic functions. This view is supported by Barberton
(2000: 4) cited in Khumalo (2007: 1) who argues that the frequent audit of
executives and bureaucrats is an indispensable instrument to achieve
transparency and accountability on how public funds are used and what value
is obtainable from the use of them.
The PFMA of 1999 assigns great significance to auditing as a function of
service quality accountability, transparency and development effectiveness.
In this way, the audit function plays a very important role in public financial
management by providing an independent audit. In an instance of noncompliance, it should account to Parliament and ensure that this is reported
to the public so that remedial action can be taken (Khumalo, 2007: 2). By
such action, the auditing profession gains the confidence of the public that it
upholds and maintains the fundamental values of society in the management
of public funding (Kusi, 2004: 1). Given the vast majority of public entities,
including SOEs and MoEs, the Auditor General can appoint or outsource its
audit function to an independent third party or professional body, which will
perform the assignment with professional competence.
•
Accountability
The introduction of the PFMA 1999 (Act 1 of 1999), as amended by Act 29 of
1999, adopted a financial management methodology. This entails focusing
on the output and responsibility rather than being rule driven by the
Exchequer Act. There is, therefore, a need for accountability of results
(Khumalo 2007: 1).
27
According to Visser and Erasmus (2013: 11), public sector financial
management legislation ensures that there is the development of a mechanism
and procedure that will permit accountability within government units. Within the
Constitution of South Africa, there is provision that mandates the National
Treasury to the transparent and sound financial control among government units
(Khumalo, 2015: 85). The above view is supported by Mulamba (2015: 72) who
states that it is the law of the municipalities in South Africa that seeks to promote
the financial transparency and accountability of local government.
2.4.2 Municipal Systems Act (MSA) 32 of 2000 as amended
In terms of the Municipal System Act 32 of 2000, as amended in the Act 44 of
2003, it seeks to:
•
cater for the elementary principles, mechanisms and processes that are
important tenable municipalities to move progressively towards the socioeconomic improvement of local communities and guarantee widespread
access to vital services that are reasonable to all;
•
define the legal nature of a municipality as including the local community
within the municipal area, working in partnership with the municipality's
political and administrative structures;
•
provide for the way in which municipal powers and functions are executed
and performed and provide for community participation;
•
establish a simple and enabling framework for the core processes of planning, performance management, resource mobilisation, and organisational
change, which underpin the notion of developmental local government;
•
provide a framework for local public administration and human resource
development; and
•
empower the poor and ensure that municipalities put in place service tariffs
and credit control policies that consider their needs by providing a framework
for the provision of services, service delivery agreements and municipal
service districts and provide for credit control and debt collection, etc.
Lee Gardyne (2005: 56) argues that the Municipal System Act 32 of 2000 was
the last essential piece of legislation in the course of establishing the principle of
partaking in local governance and development.
28
2.4.3 Municipal Finance Management Act (MFMA) of 2003
The aim of this Act is as follows:
•
To facilitate sound and sustainable management of the financial matters of
municipalities and other institutions in the local sphere of government;
•
To establish treasury norms and standards for the local sphere of government; and
•
To provide for matters connected therewith.
The MFMA of 2003 has placed local government finances on a sustainable
footing in order to maximise capacity of the municipalities to render services to
their communities with the aim of modernising budgets, accounting and financial
managements practice. Furthermore, it aims to put in place a comprehensive
governance framework, by illustrating and detaching the role and responsibilities
of bureaucrats, councillors (non-executive), executive mayor and executive or
mayoral committee. Klaus & Bahrinipour (2008: 57) emphasise that a regulatory
framework in the South African MFMA in numerous ways facilitates sub-national
borrowing. Section 5.2 within the Act encourages the actors to borrow or lend,
but there are rules that are enforced whose clear guidelines offer the supply side
of the borrowing market by municipalities’ clarity, confidence and predictions
(section 5.3). Lastly, the MFMA regulates possible disasters and prevents those
positively (section 5.4).
It is the main objective of the Act to safeguard a sound and sustainable management of the financial matters and fiscal affairs of municipalities. MoEs and 20
other SOEs established norms with standard requirements for:
•
ensuring that there is accountability, transparency, suitable lines of responsibility in the financial matters and fiscals of the municipality and its entities;
•
the administration of their assets, liabilities, income (revenue), expenditures,
and their financial transactions;
•
the process of budget and financial planning and the co-ordination of such
are within the process of organs of state and other spheres of government;
and
•
the management of borrowings and financial problems in municipalities.
29
2.4.4 Municipal borrowings
In 2000, the National Treasury issued a policy framework about municipal
borrowings. According to the Policy of Framework for Municipal Borrowings and
Financial Emergencies, Section 230 of the Constitution grants South African
municipalities the privilege of borrowing and it claims that these privileges will be
logically regulated by legislation approved by central government. In terms of
MFMA 56 of 2003, a municipality may incur long-term or short-term debt for the
purpose prescribed by the Act. Alexander (2015: 37) states that, owing to changing
conditions, municipal borrowing has grown rapidly in recent years and such
phenomena are likely to accelerate in view of the vast investment backlogs. This
phenomenon gave leeway to municipalities to continue to decentralise service
responsibilities. In addition, the Policy of Framework for Municipal Borrowings
and Financial Emergencies, Section 230 gives municipalities an equal authority
on borrowing and allows its entities the authority to borrow while the extent of the
borrowing remains a decision of the municipality itself (Alexander, 2015: 47).
Consequently, municipal borrowing remains a trend in South Africa pre-and postapartheid, as it is common at municipal level to access capital markets to fund
municipal investments (Klaus & Bahrinipour, 2008: 2).
2.4.5 Municipal-owned enterprise (MoE) borrowings
Section 66(7) (a) and (b) of the PFMA (as amended) makes provision for public
entities to borrow money. Such debt must, however, be submitted annually to the
Minister of Finance as part of the borrowing programme for the year with the
exception of MoEs. Public entities may not borrow in a foreign currency that is
above the prescribed borrowing plan, but an exception is made if the public entity
is a company in which the state is not the only shareholder (Sadiki, 2015: 46).
Despite the Policy of Framework for Municipal Borrowings and Financial
Emergencies Section 230 (2007: 12) providing MoEs with powers to borrow,
requests are accepted from the chief executive officer, authorised by the municipal council. This borrowing does not require national nor provincial approval but
provides these structures with the opportunity to comment.
30
According to the policy framework, the borrowing of MoEs must be in accordance
with: (a) the entity's multi-year business plan referred to in section 87(5) (d); and
(b) the provisions of Chapter 6 to the extent that those provisions can be applied
by a municipal entity. In terms of the Framework for Municipal Borrowing and
Financial Emergencies (2000: 17), the borrowings of municipal entities of any
such guarantees will constitute an act of municipal borrowing and the resultant
liabilities will be recorded on the municipal balance sheet as such. In the context
of the MoEs in the CoJ, the City is a sole shareholder of all the entities. Any
borrowing is, therefore, subject to the municipal council. Even though the MoEs
are independent companies formed within the framework of the Companies Act,
their financing decisions are unique compared to the private sector corporate.
MoEs internally generate revenues and receive funding through municipal
subsidy, bonds and long-term loans. Studies by Denis and Mihov (2003: 4),
Nyamita (2014: 151), Frank, and Goyal (2005: 154) concur that debt financing is
a major source of external finances, compared to equity funds.
2.5
SOURCES OF FINANCING FOR MoEs
Many factors affect obtaining external funding, especially funding from international institutions. A major factor is a good credit rating (Sadiki, 2015: 61).
External financing involves getting funds from an outside source without giving
goods or services in return (Nyamita, 2014: 50). If an entity requires external
finance, it will have to reveal additional information, in turn exposing it to special
scrutiny. This can be regarded as another form of monitoring (Moloi, 2008: 24).
With reference to the pecking order theory, entities prefer internal to external
financing and debt to equity if it issues securities (Myers, 1984: 16). According to
the pecking order theory, the entity prefers retained earnings, debt financing and
opts for equity as the last resort. External finance, particularly debt capital, which
can be raised by MoEs includes bonds, long-terms and other bank credits.
31
2.5.1 Equity financing
Sadiki (2015: 61) shows that the injection of cash or capital by Government is
referred to as equity to reduce debt and stimulate growth. Chenesai (2009: 11) says
that government grant schemes and tax incentive equity schemes may also be
important sources of external equity financing for fledging entities, especially in
strategically targeted sectors. External equity finance by state and municipal-owned
entities can be generated through the issuing of stock to the public, government or
private entrepreneurs (Adamolekum, 1999: 42).
Nyamita (2014: 50) claims that, under normal circumstances, government as a
principal shareholder in a state and municipal-owned entity may contribute to the
increment of equity to the entity or increase its shares. Despite this, private
entrepreneurs can contribute to the financing of SOEs through Initial Public Purchase
(IPO) offers. He further emphasises that financial institutions, such as banks and
insurances entities, which are shareholders in partially-owned state entities, might
opt to increase equity shareholding by acquiring more equity shares.
•
Grants
The contribution of grants to capital revenue is substantial across all categories
of municipality. A grant is, most pronounced for districts with the major power,
and rural and medium to smaller municipalities (Alexander, 2015: 35). Entities
are confronted with a concern risk if there is no annual equitable share grant
from government (Gardyne, 2005: 195). Sebapadi (2016: 15), however, argues
that government grants are linked to maintenance and current costs of historic
capital expenditure.
•
Subsidies
MoEs are expected to raise some financial support apart from that given by the
municipality. Sebapadi (2016: 14) indicates that, even in the post-1994 era,
municipalities are expected to raise their income from own rates and taxes, with
only a limited national subsidy. Furthermore, Matanga (2013: 38) argues that it
also makes it difficult to establish whether a subsidy can be classified as an
export subsidy or production subsidy. A subsidy comes as a cash injection to the
entity and does not restrict any entity from seeking additional funding particularly
debt.
32
2.5.2 Debt financing
Sebapadi (2016: 18) advocates that debt financing is widely categorised into
private and public debt, where private debt consists of both bank and non-bank
loan obligations. In the case of private lending, such an obligation is not open to
scrutiny. On the contrary, public debt financing is the process of obtaining funds
from the securities markets by issuing different types of entities’ debt stocks
(Nyamita, 2014: 5). It is within the discretion of the entity’s management to opt
between the two types of debt financing. Sebapadi (2016: 18) asserts that public
borrowers have a higher credit quality. Denisa and Mihovb (2002: 9), however,
contend that there is little evidence on the role of credit quality and none on the
role of managerial discretion in debt choice. In the context of a SOE, a common
practice of debt financing is the use of bank loans and stock-market debt
securities or instruments, such as bonds and notes (Nyamita, 2014: 51).
•
Bank loans
A loan is an amount of money borrowed from the bank, financial institution or
shareholders, which has to be paid back in full together with interest (Black,
2003: 166). An entity may choose to hold a guarantee of a non-guarantee loan,
which primarily originates from banks and financial institutions. Denisa and
Mihovb (2002: 21), however, claim that firms with profitable margins are most
likely to issue public debts instead of bank loans or other private debt.
Gildenhuys (1997), in Alexander (2015: 31), asserts that loan financing is crucial
and for local authorities to maintain a high standard of discipline they would need
to comply with five key conditions:
o
Separation of the revenue account from the loans account;
o
The utilisation of loan money for capital expenditure and not operating
revenue;
o
Use of borrowed funds for development of productive assets (income
generating assets) that are used to repay debt, where possible;
o
Not paying loans with new loans; and
o
The separation of operational and capital budgets.
Sebapadi (2016: 4) emphasises that currently bank credits and municipal bonds
are the two main financing instruments of sub-national borrowing in South Africa.
33
•
Stock market loans
The National Treasury under the South African Internal Registered Bond Act
prescribes that all bonds issued should be represented by certificates and
should be transferable by the registration in the Register. Nothing in these
conditions, however, prohibits any bonds from being held through any
settlement system or in any depository in a certificate or dematerialised form.
It simply means that all bonds issued by the municipal entity, municipality or
state entity must comply with this act prescribed by the National Treasury.
The Organisation for Economic Cooperation and Development (OECD, 2005:
32) highlights that all financing decisions in the form of the stock market is
bound to be effectively documented so that it can give effective accountability
through audits or examination by Parliament. Other financial instruments that
can easily be associated with the stock market are debenture, notes and
mortgage bond. Such, however, fall outside the scope of this study.
•
Bonds
According to the Policy Framework for Municipal Borrowings and Financial
Emergencies (18), South African bonds are issued by parastatals and are
conceptually similar to municipal securities, which are easily traded within the
existing market. According to Stilwell (2012: 54), a bond is a financial asset,
which entails the purchase of a loan from government or an entity, which,
therefore, entitles the owner of such bonds to expect interest payments. The
Bond Exchange of South Africa 2002 (BESA) reports that, in the 1990s,
SOEs issued more bonds. This was because of the expansion in the South
African financial market. The findings by Ball, Hail and Vasvari (2011)
reported that corporate entities are more likely to move in the bond market
as opposed to the equity market to raise capital for projects.
According to Klaus & Bahrinipour (2008: 6), the first bond was released in
2004 under the new system in Johannesburg and was widely praised
nationally and internationally, raising an intense debate on municipal bonds
for development financing in emerging economies. As a result, bonds are
perceived to be more viable because better terms (interest and repayment)
can be negotiated, while interest rates and other terms of bank loans are set
by the bank.
34
Balmaceda, Fischer and Ramirez (2014: 47) in Nyamita (2014: 52), however,
argue that most SOEs from developed economies prefer debt financing
through stock-market instruments, like bonds, notes and debentures, since
their stock markets are more liberalised and easily accessible by entities. The
use of bonds and bond options for arbitrage, hedging or speculation does not
only benefit the institutions that enter these transactions but is also beneficial
to an economy (Erasmus, 2014: 6).
2.5
CONCLUSION
This chapter has discussed the relevance of local government and the PFMA with
regard to the debt financing of MoEs. It has shown the relation between debt
financing and financial management in terms of PFMA. The chapter has also
explored the concept of financial management functions, their efficiency and
effectiveness that drive the smooth operation of the entity, namely budgeting,
financing, expenditure management, financial reporting, and auditing. The last
part in this chapter highlights the source of funding for MoEs limited to debt
financing. The next chapter presents the literature review.
35
CHAPTER 3
LITERATURE REVIEW
3.1
INTRODUCTION
This chapter reviews the existing literature on the theory of debt financing, and a
detailed theory on capital structure, specifically debt financing. This is followed by a
concise overview of the fundamental factors that influence debt financing and the
types of debt instruments preferred by MoEs. In addition, the chapter reviews debt
financing as a concept, the theory on debt financing, factors influencing debt
financing, types of debt financing within the MoEs, debt level within MoEs, and the
empirical findings. The views of many researchers related to the study are presented
and, subsequently, the chapter provides a section arguing, criticising or supporting
the previous empirical studies on the theory of capital structure. The conclusion from
the literature review will provide context for the best practice of capital structure and
will contribute to the available literature on debt financing concerning MoEs. The
relevant theories are trade-off, static trade-off, dynamic trade-off, pecking order,
agency theory, market timing, and other related theories. The chapter concludes with
a review of empirical studies on debt financing which is a conceptual framework in
the study.
3.2
BACKGROUND AND DEVELOPMENT OF DEBT FINANCING
In general, the debate around the capital structure of an entity has received
enormous attention over the years in the financial management fraternity, both in
developed and developing countries. According to Kasozi (2009: 14), with the
agreement of management, an entity can choose any capital structure it wants
because the significant elements that influence such a decision are easily identifiable. It is, however, argued that most entities use debt in their capital structure
primarily because its tax deduction on interest lowers the cost of debt financing and
makes debt capital the cheapest type of outside financing that is available to most
entities (Fosberg, 2004: 32). Even though historically there has been some extensive
research on debt financing, there is a need to develop an understanding of the
context of debt financing within MoEs in South Africa. Nyamita (2014: 72) argues that
there are no clear, demarcated debt financing theories used for state-owned and
municipal entities in developing countries only.
36
3.3
DEBT FINANCING
According to Goswami and Shrikhande (2001: 39), in the industrialised economies in the world since productive activities, most entities opt for debt financing
rather than equity financing, both in local and foreign countries. This gives
multinational entities the choice of financing with debt in local and international
capital markets. Denisa and Mihov (2003: 4) define debt financing as the predominant source of new external funds for entities. In supporting this view,
Nyamita (2014: 89) argues that debt financing is the major source of external
finance, when compared to equity external funding. In addition, Denisa and Mihov
(2003: 3) state that the primary determinant of the source of debt financing is the
credit quality of the issuer, and there are three new sources of debt financing,
namely banks, non-bank private lenders, and public debt offers.
An important finding that emerged in developed countries was revealed in other
studies (Diamond, 1984; Boyd & Prescott, 1986; Berlin & Loyes, Choong, et al.
2010: 108). The latter hypothesised that in terms of monitoring efficiency, private
debt financing has a significant advantage over public debt. The entity has an
option to borrow from a bank or non-banking intermediary such as insurance
entities provided it choose to acquire private debt. Findings by Agbloyor et al.
(2014: 138) and Nyamita (2014: 192) have shown that the use of public debt in
both developed and developing economies is more prominent than the use of
private debt. However, private debts irrespective of their origin such as loans are
also favoured.
Given the above, O'Brien and David (2010: 52) argue that debt financing, just like
any form of financing, has its own benefits and challenges. For example, the
benefits of debt include tax deductibility of interest and the decrease of free cash
flow problems. The costs of debt contain possibility of bankruptcy costs and
agency disputes between investors and bondholders (Fama and French (2002:
1).. Fosberg (2004: 32) argues that the major threat or challenge of debt financing
is that, if the debt increases and the entity does not service its debt, the entity
might go bankrupt.
37
3.4
THEORIES OF DEBT FINANCING
This section discusses all the theories of debt financing, which include the tradeoff, static trade-off, dynamic trade-off, pecking order, agency theory, market timing,
and other related theories as mentioned earlier. According to Modigliani and
Miller (1958: 268), the introduction of debt financing shifts the market share in a
very fundamental way because there may be different proportions with the entity
of debt in their capital structure and shares of different companies.. Even in the
same class, this can give rise to different probability distributions of return. It is
impossible to have specific types of debt financing because of the increased
global financial advancement. Nevertheless, in general, the classification of
sources of debt finance can be varied depending upon the characteristics of the
provider and the debt’s maturity period (Nyamita, 2014: 5). Agarwal and Mohtadi
(2004: 59), however, argue that entities that adjust their capital structure away
from excessive debt reduce the risk exposure of debt-equity mix, and, therefore,
lower their cost of finance. On the other hand, Gitman (2010: 289) asserts that
debt financing is generally an inexpensive form of financing compared to shortterm borrowing.
Nyamita (2014: 74) points out that, given the availability of many conditions that
may affect the debt-financing level within the entity; it is not surprising that many
different theories have been proposed after the proposition of Modigliani and
Miller. Jensen and Meckling (1976) introduced the concept of static trade-off
theory, which suggested that optimal capital structure could be achieved where
the net tax advantage of debt financing leveraged related costs, such as financial
distress and bankruptcy holding the firm’s assets and investment decision. The
literature that followed, particularly on corporate finance, pointed to the trade-off
theory, where taxation and deadweight bankruptcy are taken into consideration
(Frank & Goyal, 2009: 1). Otieno (2015: 42) propagates that under the static
trade-off framework; the firm can set a target debt-to-value ratio and gradually
move towards a refinancing point.
38
Following the reasoning of the trade-off theory, it suggests that international
diversification reduces the expected cost of bankruptcy and allows for increased
debt capacity (Abor, 2007: 41). Frank and Goyal (2004: 3), however, paint a
different picture that says that trade-off for the benefits of debts such as tax
savings mitigates agency problems against the actual cost of debt and bankruptcy risks. On the contrary, Myers (1984: 581) proposes the “pecking order
theory” in which there is preference of retained earnings, debt and then equity.
Unlike the trade-off theory, the pecking order theory (Donaldson, 1961) was later
modified and popularised by Myers and Majluf (1984), who postulate that entities
initially make use of available internal funds, like retained earnings, followed by
debt before opting for external equity. The choice of capital structure is dependent
on several factors, such as taxation, bankruptcy cost/risks, agency theory,
pecking order, and trade-off.
3.4.1 Trade-off theory
Kraus and Litzenberger introduced the theory on trade-off in 1973, and Myers (1977)
developed it and described that, when deciding on the appropriate mixture of debt
and equity, the entity will consider the cost and benefit of the debt (Luscombe, 2009:
15). Trade-off theory places more significance on taxes and disputes that the entity
pursues debt that will balance tax advantage of additional money borrowed and the
costs of financial distress that results when an entity borrows too much (Myers, 2001:
81). Myers (2001) further accentuates that the trade-off theory is easily preferred
because it explains why the entity does not use excessive debt. In addition, in terms
of the trade-off model, the major benefit of debt is the tax advantage of interest
deduction enjoyed by bondholders when they pay tax (Graham & Harvey, 2001: 210).
According to Nyamita (2014: 74), the main goal of theories on trade-off is to maximise
the entity’s value by determining the optimal debt financing level.
Following the reasoning underpinning the trade-off model, it is suggested that
international diversification reduces the expected cost of bankruptcy and allows for
increased debt capacity (Abor, 2007: 41). Frank and Goyal (2004: 3), however, state
that trade-off for the benefits of debts such as tax savings mitigates agency problems
against the actual cost of debt and bankruptcy risks. Abel (2014: 4) is of the view that
trade-off theory can envisage an adequate borrowing by the tax paying entity. It
further suggests that there will never be a one-size-fits-all approach.
39
After taking into account the trade-off model, the tax trade-off model predicts that
profitable firms will employ more debt since they are more likely to have a high
tax burden and low bankruptcy risk (Abor, 2007: 37). The effective way to lower
tax costs can be through debt financing. A greater portion of debt in the capital
structure can, however, result in maximum profitability; by decreasing the value
of the entity, it will be less attractive to invest in (Zaheer, 2011: 39). There is
consensus among researchers (Ferrão, 2011; Graham & Harvey, 2001; Myers, 2001)
about the view that predicts that profitable entities that employ more debts enjoy tax
benefits. Given the empirical evidence, Fosu (2013: 140) argues that, in spite of
several decades of research in this area, the relationship between debt financing and
the financial performance of an entity has no common acceptable conclusion.
The trade-off theory is described in two different models, namely, the static trade-off
theory and the dynamic trade-off theory. The two components differ in the way they
recognise the role of time in capital structure decisions (Elomo, 2014: 8). Jensen and
Meckling (1976) introduced the concept of static trade-off theory, which postulates
that optimal capital structure could be achieved where the net tax advantage of debt
financing leverages related costs, such as financial distress and bankruptcy holding
firm’s assets and investment decision. After the reviewing of the trade-off model’s
constraints by Frank and Goyal (2005: 150), they analysed the models into static and
dynamic trade-off theories. They, therefore, concluded that the trade-off class of
models are more promising (Nyamita, 2014: 74).
3.4.1.1 Static trade-off theory
According to Chenesai (2009: 52), the emergence of the static trade off theory was
spearheaded by the path-breaking work of Modigliani and Miller (1958). The theory
focuses on the benefits of issuing debt and envisages that an optimal target financial
debt ratio exists, which maximises the value of the firm. The static theory affirms the
notion that an entity can have an optimal capital structure, which is determined by
trading off the costs against the benefits of the use of debt and equity. In addition,
the theory highlights that the existence of corporate tax and bankruptcy risk because
of capital markets’ imperfections affects the capital structure and, therefore, the value
of the entity (Elomo, 2014: 8). The static trade off theory further predicts that
profitable entities will use more debts because they enjoy tax benefits (Otieno, 2015:
8).
40
An entity can be said to be following the static trade-off theory if a debt financing
level is determined by a single trade-off between the tax benefits of the debt and
the deadweight costs of bankruptcy (Frank & Goyal, 2005: 150). Studies by Myers
(1984) and Donaldson (1961) discovered that entities firstly desire to raise capital
from retained earnings, followed by debt, and, thereafter, they issue new equity,
when it had considered the static trade-off theory. In other findings by Kasozi
(2009: 124), the static trade-off model had both cross-sectional and time-series
explanatory power regarding the financing behaviour of listed firms. Crosssectional explanatory power is observed through the overall significance of the
designed model while time-series power is inferred using the target adjustment
model in which the firm’s debt ratios indicate mean reverting behaviour towards
an optimal debt ratio. Even though there have been more empirical studies on
static trade-off theory, different scholars, Kasozi (2009), Frank and Goyal (2005),
Otieno (2015) just to mention a few, presented different findings depending on
the scope of their study.
3.4.1.2 Dynamic trade-off theory
According to Elsas and Florysiak (2008), cited in Jardine (2014: 26), dynamic
trade-off theory is categorised when the optimal target leverage ratio varies over
time. In comparing the static and dynamic trade-off model, Krasauskaite (2011),
cited in Elomo (2015: 8), emphasises the significance of time in the capital
structure decision. The static trade-off model provides the solution of the optimal
capital structure for one period, suggesting that firms should have the optimal
capital structure in all periods. Contrary to the dynamic trade-off models, the
optimal capital structure choice in the current period depends on what is expected
to be the optimal capital structure in the next period and so on.
3.4.2 Pecking order theory
Myers and Majluf (1984) developed the pecking order theory. They suggested
that a hierarchical fashion is followed by an entity when financing their operations.
The entity uses internally generated funds, like retained earnings, followed by
debt and, finally, external equity (Abor, 2007: 31). Myers and Majluf (1984: 187)
further point out that the debt financing level is driven by the desire of the entity
to finance new investments.
41
The entity’s last resort is equity if both retained earnings and low risk debts fail.
The pecking order theory is one of the most influential theories within corporate
finance because its influence is driven from the perspective that it naturally fits
with certain facts about how the entity obtains and uses external financing (Frank
& Goyal, 2003: 218). Moreover, this theory is concerned with how information
asymmetry affects a firm’s investment and financing decision (Elomo, 2014: 9).
According to Arnold (2005: 536), the pecking order theory better explains that
profitable entities often opt for minimum debt, not because their target ratios are
low but because they do not need external financing. Less profitable entities, with
extensive investment programmes which do not have sufficient funds available
for capital investments may issue debt because it is first in the pecking order
theory. This is driven partly by the overall view that retained earnings area form
of finance that belongs to the shareholders and does not earn as much return as
it could if invested elsewhere (Gangeni, 2006: 12). Van Rooyen et al., (2014:
327), however, dispute the view, emphasising that management will rather make
decisions on the entity’s capital structure based on their preference for certain
types of finance because an optimal capital structure does not exist.
Nevertheless, Elomo (2014: 9) argues that the pecking order theory does not
specify the target, but it shows the preferences of financing.
Kasozi (2009: 51) argues that the pecking order theory also attempts to explain
the stock market’s reaction to leverage increasing and leverage decreasing
events. As debt increases, financial leverage increases (Rehman, 2013). The
short-term financing choices are merely deciding which item on the pecking order
is more desirable at a particular point in time (Naidu, 2011: 19). The most notable
contribution of the pecking-order theory is that it explains the negative relationship
between past profitability and the debt ratios of firms, a practice that the trade-off
theory fails to explain (Kasozi, 2009: 51). Furthermore, Otieno (2015: 15)
suggests that choosing debt forces managers to manage cash flow to be able to
meet the firm’s debt obligations. Debt holder, therefore, have the potential to play
a disciplinary role. Financial leverage is a measure of the level of debt a firm uses
to finance its assets. Graham and Leary (2011) aver that the pecking order model
and market for corporate control model are an indication that the use of debt
capital depends on the performance of the firm’s borrowing.
42
3.4.3 Agency theory
According to Eisenhardt (1989: 57), the agency theory is a significant model
despite being controversial. According to Abor (2007: 159), the agency theory
proposes that the separation of ownership and control in the entity bears a conflict
of interest between the entity’s shareholders and managers. This is mainly
because managers have the opportunity to use the resources of the entity in ways
that benefit themselves personally to the detriment of shareholders’ wealth
maximisation. The agency problem is particularly important in explaining capital
structure issues and gives managers numerous opportunities to exercise their
discretion with respect to capital structure decisions.
The conflict between shareholders and managers is a result of managers owning
less than 100% of the residual claim of the firm’s assets (Luscombe, 2009: 17).
Investors of the entity are, however, aware of the manager’s opportunistic behaviour.
When valuing the entity’ shares, therefore, they consider such a phenomenon
(Naidu, 2011: 20). The reputation considerations of managers have also been noted
to have an impact on the debt taken on and projects accepted (Luscombe, 2009: 18).
A solution to reduce conflict in the entity is to offer managerial incentives or more
complicated financial securities such as convertible debt (Luscombe, 2009: 17).
Managers, therefore, could look at performance in managing debt levels and vice
versa. Nyamita (2014: 77) cites that the absence of clear-cut evidence could be fairly
well explained by the inherent complexity in defining a measure of performance that
is close to the theoretical definition of agency theory costs. Debt financing requires
managers to explain the investment details to debt providers and, therefore, expose
themselves to being monitored by the debt investors (Frank & Goyal, 2005: 154 in
Nyamita, 2014: 77). Despite the importance of the capital structure and agency
problem, the area remains empirically under-researched (Abor, 2007: 160).
3.4.4 Market timing theory
Market timing entails the shifting of funds between stocks and bond or money market
instruments under management by the institutional investors in order to protect or
enhance returns on future expectations of the market’s performance (De Chassart,
2002: 3). On the contrary, De Chassart (2002: 1) elaborates that market timing
involves predicting how the market is going to move as a whole and then adjusting a
portfolio's exposure to the market either to enhance or to protect returns.
43
Frank and Goyal (2009: 1) argue that the engagement on market timing is an idea
that had become popular in publicly traded entities. Accurate timing, therefore,
requires considerable skills and periods of market because there are minimal
appreciations. The investor who gets it wrong will, however, see initial
investments
dwindle
into
insignificance
(Terblanche,
2008:
5).
It
is
understandable that one does not have to possess perfect foresight in order to
be successful at market timing. If, however, the market swings can be predicted
perfectly, the returns well above a passive buy-and-hold strategy would be
achieved (De Chassart, 2002: 1).
According to Baker and Wurgler (2002: 1), the literature on corporate finance
describes market timing as an exercise of issuing shares at an excessive amount
of money and redeeming them at a lower price. The intention is to exploit
fluctuations on a temporary basis with the cost of the equity relative to the cost of
the debt capital. Firer et al. (1987: 29) underscore that one of the major
measurements of risk related to market timing is the comprehension ration. The
number of periods that contribute to the highest gains to the total number of
periods is referred to as the comprehension ration. If an investor, therefore,
misses the best performing periods, he will not outperform an appropriate buy
and hold benchmark (De Chassart, 2002: 15). Despite this, the benefits of time
of market if possible would care more about current equity owners. Baker and
Wurgler (2002: 1), however, believe that debt-financing level decisions change
because of the cumulative outcome of past attempts to time the capital market
(Nyamita, 2014: 76).
3.4.5 Free cash flow
Given the above-mentioned tried and tested theories relating to debt financing, only
free cash flow theory has been given attention. Jensen (1986: 324) developed the
theory and suggested that managers with a lot of free cash flow in hand tend to
overinvest. They further argue that, under the free cash-flow hypothesis, managers
of the entity have the privilege to finance projects with a low return of earnings, which
might be financed by the equity or bond markets, hence reducing debt financing.
According to Yan (2006: 716), financing with debt or leases can reduce a firm's free
cash flow, thereby mitigating the overinvestment problem and reducing the firm's
agency.
44
There is a severe conflict between shareholders and managers over a payout,
especially when an entity generates substantial free cash flow (Jensen, 1986:
323). According to Ankude (1997: 8), shareholders have a right to cash flows only
when directors declare cash dividends. Shareholders do not have a decisive right
to the company's free cash flow. This situation, however, does not preclude them
from seeking protection. Shareholders have too little to gain from selecting
dividends or free cash flow over abnormal earnings as the fundamental attribute
to be valued (Francis, Olsson & Oswald, 2000: 8). Nevertheless, if the interest of
managers and shareholders were perfectly aligned, managers would distribute
all free cash flow to the shareholder (Ankude, 1997: 18). The free cash-flow
agency costs, therefore, may depend on the entity's investment opportunities
(Samuel, et al., 1996: 105).
3.5
FACTORS INFLUENCING DEBT FINANCING
Dreyer (2010: 98) suggests several factors influencing the decision about what
constitutes an ideal capital structure by identifying specific industry factors such
as the volatility of the industry’s revenues and its typical debt-to-equity ratios. In
addition, Kasozi (2009: 2) identified factors, such as firm size, industrial characteristics and management sentiment as common factors in a firm that may
influence debt-equity choice. The macro-economic environment within which a
firm operates including legal and political factors affects the firm and all
circumstances in which the firm has to grow (Mohohlo, 2013: 20). Other studies
by Otieno (2015: 41), Luscombe (2009: 9) and Nyamita (2014: 96) show that
common factors, such as profitability, age of the firm, business risk, growth
opportunities, and non-tax shields, are found to be influencing the financing
decisions of entities.
A similar study by Gwatidzo and Ojah (2009) discovered that leverage is positively related to intangible assets and firm size. Significant evidence, therefore,
indicates that firm-specific factors are consistent indicators. According to Frank
and Goyal (2009: 1), when an entity decides to use debt finance, there is some
reallocation of expected cash flows away from equity claimants in exchange for
cash up front. The factors that drive this decision remain elusive despite a vast
theoretical literature and decades of empirical tests.
45
The entity’s specific factors have remained the major focus of debt financing
studies (Kayo & Kimura, 2011: 358). As mentioned earlier, Frank and Goyal
(2009: 1), however, believe that, even though there is a plethora of theoretical
literature and years of empirical tests, the factors that influence debt-financing
decision-making remain vague.
3.5.1 Common factors within the entities
Based on the findings of Otieno (2015: 41), Luscombe (2009: 9) and Nyamita
(2014: 96), it can be argued, from their past empirical research, that entities from
developing and developed countries are faced with common factors that
influence debt financing. In the South Africa context, however, the culture
adopted may well have an impact on the business environment and the capital
structure chosen by different entities (Luscombe, 2009: 46).
3.5.1.1 Entity size
According to Abor (2007: 34), firm size has been viewed as being a determinant
of capital structure. The effect of the firm size on leverages is vague (Delcoure,
2007: 407). Moreover, the findings of Titman and Wessels (1988) and Castanias
(1983) suggest that larger firms tend to be diversified and, therefore, less prone
to bankruptcy. In addition, larger firms have a better advantage in accessing
credit markets as opposed to smaller firms. Furthermore, there is more diluted
ownership in larger firms leading to a less controlled managerial decision. Taub
(1975: 412) emphasises that larger firms have more assets and resources to fall
back on in the event of not being able to meet their debt commitments. Lenders
of larger firms are, therefore, more likely to receive their repayment compared to
those of smaller firms, reducing the agency costs associated with debt. As a
consequence of this, larger firms will have higher debts (Abor, 2007: 34).
The findings from the study of Smith (1977) based on issuance costs revealed
that small firms experienced greater floatation costs in issuing equity and
borrowing new long-term debt. They suggested, therefore, that smaller firms
prefer short-term to long-term because the fixed costs are relatively low. As a
result, a slightly negative association may be evident with respect to size and
short-term debt.
46
In addition, the reason for smaller firms having a low debt ratio is that relative
bankruptcy costs are an inverse function of firm size (Titman & Wessels, 1988:
2). Smaller firms may find it comparatively expensive to resolve information
irregularities with lenders, and, therefore, may present lower debt ratios.
If the firm follows the pecking order theory of capital structure, it is anticipated
that the size of the firm will be negatively related to leverage. On the contrary, the
trade-off theory posits that large firms face lower financial distress and agency
costs and, therefore, are able to borrow more than small firms (Dang, 2013: 176).
According to a hypothesis in the study by Byoun (2008: 3077), the size of the
firms is expected to have a positive impact on debt financing levels. In addition,
it is understood that larger firms are generally more transparent, tend to have
larger debt levels and can issue larger amounts of debt, so allowing them to
spread the issuing costs.
3.5.1.2 Age of the entity
The age of the entity is a standard measure of reputation in capital structure
models. From the standpoint of a life cycle, as the entity grows, it establishes
itself as a continuing business, and consequently increases its capacity to take
on more debt; hence, age is positively related to debt (Abor, 2007: 33). According
to Chenasai (2009: 13), there is a greater informational impenetrability problem
experienced by entities at the start-up stage and so they might not access debt
financing. As the entity becomes established and develops a trading and credit
history, reputation effects alleviate the problem of moral hazard, facilitating
borrowing capacity. The prediction by trade-off theory is that older entities are not
faced by the bankruptcy problem and lower agency problem. They are also more
established, have a better reputation, credit history, and a greater tendency to
choose safe investment projects (López & Sogorb, 2008; Frank & Goyal, 2009 in
Elomo, 2014: 11).
Luscombe (2009: 49) asserts that the principles of the pecking order theory
suggest a negative relationship, and this is because older surviving entities tend
to have higher retained earnings. The entity will firstly utilise retained income
before incurring any debt if it follows the hierarchical ladder of preference.
47
To obtain debt at a cheaper rate, on the other hand, entities have built reputable
relationships with lenders. Nevertheless, Elomo (2014: 11) argues that pecking
order theory postulates that more matured entities tend to have higher cash flow
generated internally over the years, so a lower debt level is expected. Contrary
to that, smaller entities cannot retain earnings as easily as bigger entities can.
This theory postulates an inverse relationship between age and leverage. Because small entities are riskier and new to the market, they are expected to rely
more on short-term debt. Although short-term debt is relatively more expensive,
the new entities generally try to avoid any long-term commitments (Luscombe,
2009: 49).
3.5.1.3 Profitability
The entity’s financial performance, proxies as profitable, has been identified as a
potential determinant of capital structure from the number of theoretical foundations (Mkhawane, 2010: 18). According to Abor (2007: 36), the pecking order
theory explains the relationship between the entity’s profitability and capital
structure. As indicated earlier, therefore, the theory on pecking order by Myers
(1984: 16) posits that entities prefer internal sources of finance to external
sources. The least sensitive (and least risky) compared to the one most sensitive
(and riskiest) will be the order of the entity’s finance preference. This is owing to
asymmetric information between corporate insiders and less well-informed
market participants. By this phenomenon, profitable entities, which have access
to retained profit, can rely on such profits as opposed to depending on outside
sources-debt (Abor, 2007: 37). According to the perception of agency costs, it will
be expected to be profitable provided the limit by debt financing is more valuable
since those entities are prone to having stern free cash-flow problems (Jensen,
1986: 324).
According to Chenesai (2009: 13), an entity with a high profit rate, ceteris paribus,
would maintain a relatively lower debt because of its ability to finance itself from
internally generated funds. The decision to use external equity could be affected
by profitability, which means that the higher profits the entity gets the more
availability of internal equity there is. Hence, because of the greater availability of
internally generated funds related to their high profitability, profitable industries
tend to have lower external equity in their capital structure (Chenesai, 2009: 12).
48
On the contrary, Frank and Goyal (2009: 7) argue that it is believed that profitable
entities might face low expected costs of financial distress and that interest-tax
benefits are more significant. Despite the positive evidence presented by studies
conducted, it is quite clear that there is still a continuous contrary finding by
scholars.
Among those who disagree with the above arguments is Elomo (2014: 12) who
argues that, in the context of trade-off theory, entities are expected to possess
stable cash flows and more debt serving capacity. The increase in debt and stable
cash flows provides more benefits as interest payments are tax deductible and
result in a reduction in the cost of capital. In addition, Luscombe (2009: 27)
emphasises that there is an expectation of a positive relationship between
leverage. Firstly, bankruptcy costs decline as profitability increases. Secondly,
interest tax shields are entities that are more desirable to profitability. Thirdly,
increases in debt reduce agency costs (Jensen & Meckling, 1976; Easterbrook,
1984; Jensen, 1986). The context of such tax benefits is, therefore, not applicable
to MoEs because they are exempt from taxation.
3.5.1.4 Business risk
Luscombe (2009: 21) describes business risk as the hazard of financial distress
and bankruptcy that face the entity. He further argues that business risk appears
to be the most evident in related literature. Business risk refers to the effects of
doubts in the environment with regard to the earning ability of an entity. It is,
therefore, concerned more with the operating activities of an entity (Elomo, 2014:
23). According to Abor (2007: 38), one of the primary determinants of the entity’s
capital structure is the level of risk. The more the entity is exposed to such risk,
the more are its incentives to reduce its level of debt within its capital structure.
According to Mkhawane (2010: 20), the level of business risk associated with any
entity’s operations is expected to result in earnings variability. From this
perspective, an entity’s risk serves as a proxy for the probability of default on debt
service obligations and increased susceptibility to financial distress. Elomo (2014:
23), however, concurs that the more the volatility of earnings, the higher the
probability of bankruptcy arising from default on payment of interest.
49
Frank and Goyal (2009: 10) emphasise that the entity with the more volatile cash
flow should use less debt when it is faced by higher expected costs of financial
distress. In addition, volatile cash is equally capable of lowering the probability of
the tax benefits being used. Consequently, higher risk should result in low debt
financing under the trade-off theory.
According to Luscombe (2009: 21), the trade-off theory predicts a negative relationship between leverage and business risk. It is believed that a negative
relationship exists because the entity financed by equity fixed interest commitments has the option of not paying out dividends in times of financial difficulties.
This implies that the debt-financing level of an entity decreases with the increase
in earnings volatility leading to an expected inverse relationship (Nyamita, 2014:
83). Regardless of a number of studies in agreement with the entity’s risk as the
significant determinant of corporate debt policy, empirical investigation has led to
contradictory results (Abor, 2009: 39).
3.5.1.5 Corporate tax rate
The corporate tax rate indicates the tax shield benefit of the debt and is expected
to yield a positive outcome related to leverage (Joeveer, 2013: 298). In addition,
Antonczyk and Salzmann (2014: 136) further emphasise that debt financing
should be influenced by the corporate tax rate given the fact that debt-interest
payments are typically tax deductible whereas dividends payments are not. The
higher the tax rate, therefore, the higher interest tax benefits of debt will be. The
predictions by the trade-off theory suggest that, for the entity to take advantage
of higher tax shield, it will have to issue more debt when the tax rate is higher
(Frank & Goyal, 2009: 9). The findings by Graham and Harvey (2001: 210) show
that, in large entities, tax advantage is more significantly regulated and dividends
paying out entities with large corporate tax and high tax incentives use debt.
There was, however, too little evidence found to suggest that an entity considers
the impact of personal taxes when deciding on its debt policy.
According to Luscombe (2009: 11), taxes could play an important part in the decision relating to financing because interest is a tax deduction. With lower tax
rates, therefore, debt financing could become less attractive.
50
The corporate tax of an entity is widely influenced by the entity’s optimal capital
structures because the interest tax shield is one of the primary benefits of debt
financing. Before and during the period, 2001 to 2005, the corporate tax rate in
South Africa differed when compared to other countries. The corporate tax rate
changed from 48% in 1993 to 35% in 1997, and then decreased to 30% in 2000.
These tax rate variations may influence the benefits of debt to the company
(Luscombe, 2009: 11). The South African state entities and MoEs, therefore,
might have a minimal interest tax shield benefit because they receive government
tax exemptions and grants. This gives the entities the opportunity to use or opt
for the use of less debt financing.
3.5.1.6 Entity’s tangible assets
Tangible assets play a significant role in the entity’s access to debt financing
(Fosu, 2013: 13). Booth et al. (2001) and Campello (2006) support this view. In
addition, Kayo and Kimura (2011: 360) suggest that because the collateral
capability of tangible assets in place tends to increase debt-financing levels it is
asset intangibility, which plays an important role in debt-financing decisions.
According to Rajan and Zingales (1995: 1454), assets’ tangibility or the collateral
value of assets held by an entity has been found to be a determinant of leverage.
Entities with more tangible assets, therefore, can borrow more and more cheaply
because tangible assets are used as collateral for the debt (Luscombe, 2009: 73).
Complementary to the above, Kasozi (2009: 42) emphasises that the assets
tangibility can be measured as a fraction of its assets. Moreover, the ratio of
tangible fixed asset to total asset is seen as the appropriate measure of collateral
value, and this is operationalised as the tangible fixed assets of the firm divided
by total assets (Abor, 2007: 82). As a result, there is a consistent agreement
among researchers, which suggests that there is an existing positive relationship
between tangibility and the leverage of the entity. The existing positive
relationship between asset tangibility and leverage demonstrates that tangibleasset-rich entities use more debt than entities that rely more on patents, copyrights and other intangibles do (Frank & Goyal, 2005: 21). It justified that entities
that are challenged in providing collaterals are exposed to higher interest or may
be forced to issue equity instead of debt finance, and this suggests a positive
relationship between tangibility of assets and debt financing (Nyamita, 2014: 81).
51
Another essential point in support of this view is that the pecking order theory
makes opposite predictions since low information asymmetry associated with
tangible assets makes equity issuances less costly (Frank & Goyal, 2009: 9). In
contrast, other studies, like those of Nyamita (2014) and Luscombe (2009), point
out a negative level of relationship between leverage and tangibility. There is also
inconsistency with the argument of Frank and Goyal (2003: 8) that says that
regulated entities are highly leveraged because there tend to be more stable cash
flows arising out of the lower business risk, which reduces the expected costs of
financial distress. This might be applicable to MoEs with more tangible assets
because regulations, policy and procedures may allow them to engage in lower
business risk in order to be free from financial agony.
3.5.1.8 Growth opportunity
According to Luscombe (2009: 28), the determinant of growth opportunity is symbolic
of the future growth prospects of the entity. Furthermore, it incorporates the potential
future market share and sales growth. Equally important, Deesomsak, Paudyal and
Pescetto (2004: 393) stress that trade-off, agency theories predict a negative
relationship between the entity’s growth and debts since an entity’s growth increases
financial distress and the agency’s cost of debt. This is, however, in contrast to the
findings of Shuetrim et al. (1993) and those of Barbosa and Moraes (2003) who argue
that the positive relationship between growth opportunities was owing to the fastgrowing entities, which, in turn, rapidly depletes their internal finances.
3.5.2 Macro-economic factors
Just as other factors, macro-economic factors play a significant role in the capital
structure decision of an entity. More importantly, Drobetz, Pensa and Wanzenried
(2006: 23) assert that macro-economic conditions affect entities’ adjustment speed
on their capital structure. The fluctuations of macro-economic conditions, precisely
at an economic trough and peak, compel entities to respond to the change in their
growth opportunities by adjusting their capital structure (Yeh & Roca, 2010: 2).
Overall, empirical studies by Frank and Goyal (2009: 3), Nyamita (2014: 85) and
Drobetz, et al. (2006: 23) present a coherent picture that inflation rate, gross
domestic product (GDP), interest rate, fiscal policy, and economic growth are
common factors that influence an entity’s debt financing.
52
3.5.2.1 Inflation rate
Inflation rate is an important component of a macro-economic factor that affects
the opportunity cost of funds to borrowers (Boyd & Prescott, 1986: 391). Another
essential point by Baltacı and Ayaydın (2014: 50) is that the inflation rate is one
of the main indicators of a country's stability. As a result, an increase in the
inflation rate causes doubts about the economic conditions. Such doubts might
cause entities’ stability to repay their debts. Higher rates of inflation, therefore,
will lead to the reduction of the real returns perceived by savers and to lower
opportunity cost of funds to borrowers (Boyd & Prescott, 1986: 392). Moreover,
higher inflation decreases the benefits of leverage because of higher bankruptcy
costs of debt imposed on firms (Gungoraydinoglu & Öztekin, 2011: 1467). It is
expected, therefore, that inflation rates be negatively related to an entity’s debt
financing levels (Nyamita, 2014: 86).
Other studies, however, have considered the relationship between inflation and
debt financing to be positively related. For instance, in support of a positive relation, Frank and Goyal (2009: 11) emphasise that, if managers issue debt when
the expected inflation rate is high relative to current interest rates, there may be
a positive relation because of market timing in debt markets. In addition, owing to
the higher real value of tax deductions on debt, the expected inflation will predict
positively related to debt financing (Jõeveer, 2013: 295). According to the
worldwide inflation data reports, South African inflation between 2010 and 2016
has been unstable and such anticipated changes in the rate of inflation cause an
impact on the debt financing fraternity including MoEs.
3.5.2.2 Gross domestic product (GDP)
The country’s total output of goods and services is referred to as the GDP. Its
residents and non-residents must produce such goods and services within the
country’s borders within a year (Smith & Todaro, 2003: 796). Furthermore, Mato
(2015: 5) emphasises that the measure of the economy of a country is the total
market value of all goods and services produced in a country in a given specific
period, which result in a GDP. The application of the GDP measure lies on its
simplicity. Moreover, the GDP has been accepted globally as the standard
measure of macro-economic activities.
53
Equally important, the GDP has been positioned as the core economic and social
management tool over the years. As a result, it has an influence on social analysis
formulation and decision making, particularly on debt financing (Schepelmann et
al., 2010: 19).
Other studies consider the relationship between GDP and debt financing to be
positive. Jõeveer (2013: 298) supports the view that the GDP growth rate, used
as a proxy for growth opportunities, should result in a positive relationship with
debt-financing levels of entities. Mokhova and Zinecker (2014: 533) believe that
an increase in the entities’ profits might be a result of the growth in the GDP and
consequently boost the economy. Recent developments in the 2008 recession
disaster, however, have had an impact on GDPs. The negative impact of the
recession was, among other things, seen in exchange rate volatility, underperforming international markets measured by market capitalisation as a percentage
of GDP (Mogaladi, 2016: 1). Given the set objectives and mandate being pursued
by MoEs, GDP may have no effect on their access to finance, including debt
financing.
3.5.2.3 Interest rate
Interest rate plays a crucial role in determining the effective cost of borrowing and
the cost of the deal (Mkhawane, 2010: 51). Moreover, Jardine (2014: 40)
emphasises that the level of interest rates is an important consideration for debt
issuances. In the event, therefore, where the interest rate increases the existing
equity and existing debt drop in value cascades by more than the latter, and,
therefore, an entity is left seemingly highly leveraged (Kasozi, 2009: 38). More
importantly, interest rates have a direct effect on entities, which are highly levered
or leveraged (Cook, 2013: 19). In this case, the level of interest rates is expected
to be positively related to leverage (Nyamita, 2014: 86).
According to Chenesai (2009: 67), interest rates fluctuate with the prime rate, and
this can be generally used for financing in working capital. More importantly, it
can be used equally when bridging finance is required where a gap exists
between a long-term debt and the long-term source of finance becoming
available.
54
An entity can, however, choose an optimal debt-equity ratio that minimises its
total cost of capital owing to the interest rate and the equity yield, which forces an
entity to pay increasing functions of the firm's debt-equity ratio (Feldstein, Green
& Sheshinski, 2013: 45). In the same way, interest rates are incorporated in the
expectation of inflation. As a result, entities are most likely to adopt debt financing
from equity when interest rates increase (Deesomsak, et al., 2004: 395). The
South African interest rate has been fluctuating over the past few years. It will be
interesting to determine its effect on debt financing within MoEs using average
interest rate as proxy.
3.5.2.4 Fiscal policy
The fiscal policy is an instrument that contributes to the stabilisation of the
economy to the extent that it stabilises output, income and demand. Even in the
economic recession, fiscal policy can maintain or increase the government
expenditure, even by reducing tax revenue (Swanepoel, 2003: 6). The term fiscal
policy is often limited to the application of government spending, taxation and
government policies to attain macro-economic objectives (Malan 1998: 53).
According to KoekeMoEr (2013: 90), it has been shown that for many decades
the discretionary fiscal policy has been disregarded as the fundamental macroeconomic policy tool because monetary policy was considered a more effective
means of stabilising the economy.
Arestis and Sawyer (2010: 329), moreover, emphasise that fiscal policy has
brought about unnecessary spending and that the associated excessive budget
deficits bring about unsustainable public debt. The use of fiscal rules, therefore,
has been proposed by many to limit these tendencies. Consequently, within the
South African context, fiscal policy is at the centre of economic policy (Merg,
1993: 21). Broadly speaking, the focus of fiscal support should shift gradually but
meaningfully from debt incentives to equity incentives (Chenesai, 2009: 90). Thus
far, there is little possibility or risk that the public debt in South Africa will become
too high in the near future owing to fiscal policy (KoekeMoEr, 2013: 11).
55
3.5.2.5 Economic growth
MoEs are critically important in the economic growth of most countries and particularly in South Africa. Stiglingh (2015: 33) argues that economic growth is
instigated by improvements in the quantity and quality of the factors of production
that a country has available, inter alia, land, labour, capital and entities. To
promote economic growth, these factors should be financed appropriately in
order to contribute to the overall production success. More importantly, debt
financing with the SOEs is a significant function of public sector financial
management; it can, thus, be used as major source of revenue and an aspect
economic growth within the public sector (Nyamita 2014: 26; Alfaro et al., 2004:
322; Rioja & Valev, 2007). From an economic growth perspective, South Africa
is one of the highly ranked emerging African economies (Nyasha, 2014: 21).
Given that South Africa’s economic growth is unstable, it will be important to find
its effect on the debt financing of MoEs.
3.6
TYPES OF DEBT FINANCING WITHIN MoEs
According to Dhawan (2001: 270), sources of capital are dependent to some
extent on whether an entity is developing or maturing. Debt financing is regarded
as any funding medium that has a contractual claim on the entity resulting in tax
deductibility. The repayment has a fixed life and has a priority claim on cash flow
in both operating and bankruptcy periods (Damodaran, 1999: 214). Traditionally,
there are various sources of external funding particularly debt financing within the
MoEs, such as public and private debt, which include long-term and short-term
debt and other categories of debt.
3.6.1 Public and private debt
There has been a simultaneous high level of external borrowing and massive
outflows of private capital experienced by developing countries. Such phenomena have been noted in sub-Saharan Africa (Ndikumana & Boyce, 2002: 3). In
the same way, since the early 1980s, the international community has turned its
attention increasingly towards the debt problems experienced by sub-Saharan
countries (Siegruhn, 1996: 50).
56
The estimates made by Boyce and Ndikumana (2001) from 1970 to 1996 show
that a cohort on these countries’ net creditor to the rest of the world in the sense
that accumulated capital flight exceeds the stock of external debt. In this instance,
debt financing is easily classified under different categories as public and private
debt by entities. The same categories were once referred to as public versus
private debt (Chen, Cheng & Lo, 2013: 755).
Public debt financing is the process of obtaining funds from the securities markets
by issuing different types of debt stocks of entities. In general, such debt stock is
referred to as bonds or notes by entities (Nyamita, 2014: 89). According to
Altunbas, Kara and Margues-Ibanez (2010: 437), corporate bonds and syndicated loans have increased comprising 94% of all public funds raised in the
European capital markets while public equity issuance accounted for only 6%. In
the context of the Johannesburg Securities Exchange (JSE), the debt capital
market is liquid and well developed in terms of the number of participants and
their daily activity with a daily trade of about R25 million. Since the issue of first
Corporate Bond in 1992, more than 1500 debt instruments have been listed on
the JSE. As a result, liquidity remains relatively low compared to government
debt, but issuance keeps growing. On the contrary, other bonds, like government
bonds, are listed at more than R1 trillion and these instruments account for 90%
of all liquidity reported to the JSE. The study by Ojah and Pillay (2009) analysed
the key effects of external public debt markets and found that there is indeed a
strong public debt market that influences the entity’s choice of leverage that it
does not apply equally to all entities.
On the contrary, private sources of debt financing can be clearly classified as
banks, credit unions, consumer finance companies, commercial finance companies, trade credit, insurance companies, factor companies, and leasing companies (Chenesai, 2009: 65). The private debt, therefore, is made up of bank
loans and amounts payable to suppliers of goods and services and other common
types of private debt are the derivative financial instruments (Nyamita, 2014: 90).
In brief, highly leveraged entities opt to secure greater public debt rather than
bank loans or other forms of private debt.
57
3.6.2 Long-term and short-term debt
Several studies have categorised the debt financing of an entity in two forms,
namely long-term and short-term debt. Similarly, Abor (2007: 29) supports this
view and states that the entity’s debt financing comprises short-term debt and
long-term debt. The order of preference between long-term and short-term debt
depends on the needs of the entity. In the analysis of South Africa’s capital
structure, Mohohlo (2013: 69) indicates that entities in South Africa prefer to use
more long-term debt rather than short-term debt. Long-term debt is the kind of
debt where an entity has an obligation to settle the debt in a period beyond the
entity’s accounting period (Nyamita, 2014: 90). Complementary to this, short-term
debt is the amount owed by the entity repayable with the entity’s accounting
period (Hall et al., 2004: 95). Siegruhn (1996: 4) argues that, in sub-Saharan
African countries, the use of short-term debt by entities has increased
considerably. MoEs tend to use long-term debt financing. This study will establish
reasons why municipal entities choose short-term debt instead of long-term debt
as expressed in their statement of their financial position.
3.6.3 Other debt categories
There is consensus among researchers regarding the categorisation of types of
debt other than those discussed above. According to Nyamita (2014: 90), lease
financing, provisions and deferred tax liability are regarded as other special types
of debt. Lease financing in the past played a significant role in meeting the new
capital equipment of major industrial entities and it was used primarily by high risk
and capital poor entities (Riordan & Duffy, 1969: 763). Lease financing plays a
significant role in entities. Nyamita (2014: 90) defines lease financing as a mutual
contractual agreement between two parties where the lender identified on the
contract as the lesser grants the individual (entity) or a group of individuals
leasing the product or equipment and the lessee has the ability to operate the
equipment for an agreed time while making monthly payments as per the
agreement. It is also interesting to note that other entities have a habit of using
lease financing to reduce agency cost of debt (Robicheaux, Fu & Ligon, 2008:
409).
58
According to IAS (37.14), a provision is a present obligation (legal or constructive)
with uncertain timing and amount arising from a past event where payment is
likely expected to result in an outflow of economic benefit from the entity.
Notwithstanding the definition, it is important to state that liability provisions differ
from the determination of one entity to the other. Moreover, there is no compelling
reason for entities to have common liability provision accounts as a line item in
their statement of financial positions because the services they offer are unique.
According to the requirements in the Statement of Financial Accounting Standards
No. 109: Accounting for Income Taxes (FASB 1992), deferred tax liability is made
up of temporary differences between the entity’s accounting and tax carrying
values, the anticipated and enacted income rate, and the estimated tax payable
for the current year. So long as the entity follows a regular investment policy, the
deferred tax liability would be maximised indefinitely as long as the investments
generate new temporary differences at least as large as the temporary
differences that reverse (Davidson, 1958: 179).
3.7
DEBT FINANCING LEVELS WITHIN THE ENTITIES
Leverage ratios are the most important factors that determine the level of debt
financing within the entities. There are two types of leverage ratios, namely book
leverage and market leverage (Chen, 2013: 4). Despite the vast empirical
research conducted in relation to both factors, researchers remain in disagreement to date. According to Nyamita (2014: 91), some researchers have considered book leverage as being a proportion of the entity’s debt finance to the
total book value of the entity’s assets. On the contrary, others consider market
leverage as the proportion of entity debt to the market value of the entity. The
latter view is supported by Frank and Goyal (2009: 18) who argue that there is a
mechanical negative relation when market leverage is used but not when book
leverage is used.
3.7.1 Book leverage
Chen (2013: 3) defines book leverage as the amount of book value of capital
divided by the bank’s total assets. Another essential point by Nyamita (2014: 91)
is that, as a measure of the debt financing within an entity, book leverage is the
proportion of an entity’s debt financing to the total value of assets.
59
Given the aforementioned definitions, Fernandez (2007: 1) argues that they value
an entity when its debt policy is determined by a book-value ratio. He further emphasises that, when managers have a target capital structure, it is usually in book
value terms, in large part because it is that to which the credit rating agencies pay
attention. With regard to Chen’s (2013: 3) argument above, the book leverage’s
calculation lies on the book value of capital which is an accounting measure of
the net worth of an entity. Nevertheless, the statement of financial position might
reflect an inaccurate assessment of the true value of an entity’s equity.
3.7.2 Market leverage
Liu (2009: 605) defines market leverage as the book debt divided by the market value
of the entity’s assets. Along with the equity of an entity, the market leverage value of
equity is equal to the market price per share times the number of shares outstanding
(Chen, 2013: 3). In addition, in context of debt financing, market leverage can be
used to measure the entity’s debt level, and it is the proportion of entity debt to market
value of the entity (Nyamita, 2014: 92). There is consensus among researchers
regarding the definition of market leverage. Chen (2013: 3), however, argues strongly
that market leverage can be viewed as a quality adjustment of book leverage through
a refinement of the numerator (book value of equity) rather than an elaboration on
the denominator (book value of asset) of the book leverage ratio. Advocaters of
market leverage argue that the book value used to balance the left-hand side and
the right-hand managerially relevant numbers (Frank & Goyal, 2009: 2).
3.8
EMPIRICAL FINDINGS
Clearly, there are plausible arguments relative to the various factors affecting the
financial decision of an entity. In different empirical studies, there has been a
presentation of various results particularly on debt financing. While the debate
around the factors influencing debt financing remain an ongoing phenomenon among
researchers, within the entity specific factors, there are variables identified by
researchers as significant factors influencing the choice of capital structure of an
entity, namely business risk, entity size, asset composition, profitability, growth
opportunities, and age of the entity (Luscombe, 2009: 21). In addition, other
researchers have identified macro-economic factors, such as inflation rate, GDP,
interest rate, fiscal policy, and economic growth, which impact on the debt financing
of an entity.
60
According to Nyamita (2014: 86), the financial strategies of the entity, including
debt-financing decisions, are influenced externally by regional and national
macro-economic factors. Subsequently, Frank and Goyal (2005: 156) in Nyamita
(2014: 96) present evidence around debt financing in different parts: the
aggregate level; cross-sectional evidence on debt financing; debt-financing
changes; debt-financing factors; and the test of the two main debt-financing
theories (pecking order and trade-off theory).
3.8.1 Empirical findings on variables influencing debt financing of
an entity
Given the above listed factors (section 3.4.1) that are set to influence debt
financing, researchers agree that such variables have an influence on debt
financing. Most studies have identified the pecking order theory that predicts a
negative relationship between debt financing with those identified factors
(Deesomsak, Paudyal & Pescetto, 2004: 398; Antoniou, Guney & Paudyal, 2008:
73; Frank & Goyal, 2009: 26). On the contrary, the trade-off theory postulates that
the same factors have been proven to be positively related to debt financing.
Despite the broader agreement regarding the positive or negative impact that
those factors have on the determination of capital structure, particularly debt
financing, our empirical investigation will lead to a contradictory view or common
result with other studies conducted before. For instance, Mokhova and Zinecker
(2014: 534) argue that, in different studies, there are diverse results depending
on the type of entity, its nature and the economic and political environment of the
country.
3.8.2 Empirical findings on the macro-economic factors
It is in light of the disparities from previous studies that this study seeks to establish empirical evidence on the role and relationship or lack thereof between
the debt financing and macro-economic factors. Generally, it is important to note
that macro-economic factors play a pivotal role in the determination of debt
financing of an entity. According to Nyamita (2014: 103), in most studies on
macro-economic factors of debt financing, a significant positive or negative relationship has been found between the entity’s debt financing levels and macroeconomic factors.
61
Baltacı and Ayaydın (2014: 54) are among the few researchers who found a
positive relationship between some macro-economic factors, such as GDP and
the entity’s debt financing, while, in contrast, Kayo and Kimura (2011: 367) and
Jõeveer (2013: 306) found a negative relationship.
When it comes to other macro-economic factors, such as inflation rate and interest rates, there are inconsistent empirical findings from different researchers.
For instance, only Frank and Goyal (2009: 26) found a positive relationship between inflation and interest rate and debt financing, while Antoniou, Guney and
Paudyal (2008: 32) and Mokhova and Zinecker (2014: 534) found a negative
relationship. It is important to continue investigating the role and impact of macroeconomic factors on the debt financing of the entity.
3.8.3 Empirical findings on levels of debt financing within the entity
According to Mohohlo (2013: 26), there are other factors, such as external economic blows, that may have a significant impact on entities with debt levels that
are similar, even though some of these entities might have debt levels that are
perceived as being conservative. Another essential point suggests that older
entities with well-established reputations of debt repayment would have lower
costs of debt financing, and, therefore, higher debt levels (Luscombe, 2009: 19).
Chen and Zhao (2004: 24) argue that it is unlikely for an entity to reduce its debt
levels when its bankruptcy levels (or levels of financial distress) are moderate or
low. It is, therefore, possible that bankruptcy risk or the levels of financial distress
were minor with regard to warranting a reduction in the debt levels of listed
entities during this period (Kasozi, 2009: 130). Chenesai (2009: 82) believes that
the above results from various sources are evidence confirming that an entity’s
size has a positive relation to the debt level. Nevertheless, in terms of agency
cost theory, entities are expected to have the least debt and they, therefore,
depend on internal equity and that debt levels will gradually increase as the entity
develops and becomes established (Elomo, 2014: 11).
62
3.9
CONCLUSION
This chapter has made a thorough valuation and review of empirical research.
Against this background, we can conclude that specific factors mentioned above
in 3.5.1., which are common factors with the entities (size of the entity, age of the
entity, profitability, business risk, entity tax rate, entity’s nature of assets
(tangibles) and growth opportunity) have an influence on the financial decision of
the entities, particularly with regard to debt financing. Furthermore, macroeconomic factors equally have an impact on the entity’s preference for debt
financing, and such a decision is based on the type of debt the entity uses and
the extent of its usage. In addition, it is interesting to note that most past empirical
studies have shown a positive, negative or no significant relationship between the
specific factors of macro-economic and debt financing.
A considerable amount of literature has published alternative findings (Sebapadi,
2016; Nyamita, 2014; Jõeveer, 2013; Mkhawane, 2010; Luscombe, 2009;
Chenesai, 2009; Frank & Goyal, 2009), which stem from different methodological
approaches. Although there are methodological differences, this constitutes an
immediately inherent weakness owing to selection bias, which includes the
proxies used for the determination of debt financing levels. They will, therefore,
be located in the literature of debt financing, the debt level as well as the type of
debts within the MoEs. The chapter has presented a comprehensive account of
past empirical literature relating to debt financing. A detailed examination of the
methodology applicable to this study will be presented in the next chapter.
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CHAPTER 4
METHODOLOGY
4.1
INTRODUCTION
This chapter provides a comprehensive overview of the research methodology
applied to this study. In order to achieve the research objectives as stated in
section 1.5 and to answer the research questions of the study as reflected in
section 1.4 of this report, a logical, systematic and scientific process was followed
to produce required data for the purposes of the analysis and presentation of
significant findings. This chapter outlines the research paradigm, design and
methodology for the study as well the rationale for choosing the research design.
In particular, this chapter outlines the target population, sampling, measuring
instrument, data collection, and method of analysis used for the study. The
chapter concludes with a discussion of the data and presents the validity and
reliability of data, together with the delimitations, limitations and ethical
considerations.
4.2
RESEARCH PARADIGMS
According to Matukane (2016: 52), research paradigms remain the anchor of the
research process undertaken in a field of study. Babbie (2007: 32) describes a
research paradigm as a model or framework for observation and undertaking
shaping both what we see and how we understand it. Moreover, a research
paradigm is described as being a completely coherent assumption and premise,
which determines the processes used to undertake a study. Researchers like
Saunders et al. (2009), Easterby-Smith, Thorpe and Jackson (2008), and Collis
and Hussey (2003) classified research paradigms into two categories, namely
positivists (positivism) and non-positivism or naturalists (interpretivism). They
further emphasised that the positivistic paradigm reflects statistical research
designs while non-positivism or naturalists (interpretivism) reflect a qualitative
research design.
64
Regarding research paradigms, other authors, such as Healy and Perry (2000:
118), posit that there are four research paradigms. These are positivism, critical
theory, constructivism, and realism. In support of this, Neuman (2011: 165) argues that quantitative studies are underpinned by positivist principles. In fact,
most positivists use quantitative approaches. As indicated earlier, this study is
quantitative and positivist in nature. The following sections provide a detailed
discussion about positivism.
4.2.1 Positivism approach
Altinay and Paraskevas (2008: 67) describe positivism as being a research
paradigm in which the deductive approach and measurement of quantitative data
are used in order for the researcher to identify relationships that predict human
behaviour. Most often, positivistic research studies use highly structured methods
to enable them to facilitate replication (Saunder et al., 2012: 135). In addition,
Jonker and Pennink (2010: 29) believe that positivism is anchored on the idea
that scientific action produces important concepts. It is observed more often,
however, that practically this is not the case and can primarily be attributed to
people’s resistance to change. As indicated earlier, this study followed the
positivist research. Its aim was to determine the factors that influence the debt
financing of MoEs for the CoJ. Data was collected from all MoEs of the CoJ to
test for relationships or associations amongst variables.
4.3
RESEARCH DESIGN
A research design is described as the plan that the researchers follow to answer
the research question (Saunders et al., 2012: 159). Furthermore, Creswell (2014:
3) defines research designs as the plans and the techniques for a research study
that span the decisions from broad assumptions to detailed methods of data
collection and analysis. Essentially, the researcher develops and designs a
structure or framework to deal with a specific research problem or opportunity
(Tustin et al., 2005: 90). Tustin et al. (2005: 82) and Struwig and Stead (2013: 6)
divided research designs into three categories, descriptive research, exploratory
research and causal research.
65
Christensen et al. (2014: 46) describe descriptive research as providing a broad
description of a phenomenon, event, or situation. Exploratory research involves
the evaluation of or searching for an understanding of a new interest or subject
of study (Babbie, 2007: 87). In contrast, casual research investigates whether
one variable causes the value to another variable (Tustin et al., 2005: 87). For
the purpose of this study, a mixture of descriptive and correlational research
designs was used to establish underlying trends. In light of the above statement,
it is important to note that the study does not require the examination or
establishing a new subject of study. The exploratory design was, therefore, never
considered. According to Picardi and Masick (2014: 131), the combination of both
descriptive and explanatory research designs reduces the limitations of nonexperimental research through the inclusion of independent variables and
dependent variables. Precisely, according to the authors, the application of descriptive research approach is used to analyse the different types of debt and the
extent of debt financing substantively preferred.
Saunders, et al. (2009: 140) describe explanatory or casual research as an approach that establishes correlational relationships between variables (independent and dependent variables). To corroborate this, Tustin et al. (2005: 87) explain
that causal studies investigate whether one variable causes or determines the
value of another variable. The current study used independent variables (see
Table 4.3) to establish correlational relationships between debt financing (see
Table 4.2) and various independent variables, within the framework of the capital
structure of the MoEs. Independent variables comprised all factors that influence
debt financing and its debt level.
4.4
METHODOLOGICAL APPROACHES
Churchill (1998: 203), Brynard and Hanekom (2006: 35), and Creswell (2014: 3),
describe research methodology as a process indicating the actions and sequences of how data are collected and interpreted for research projects. The
authors also discussed how research methodology helps in outlining the
processes taken in identifying the target population and the determination of the
quantitative or qualitative nature of data. There are three known and used
research methodologies, namely qualitative, quantitative and mixed methods.
66
According to Mogaladi (2016: 34), quantitative research relies on statistical
analysis whereas qualitative research depends on thematic analysis. The author
further indicated that there are key features that distinguish qualitative from
quantitative research. In qualitative research, non-numerical data are collected to
answer a research question (Christensen et al., 2014: 46). Equally important,
Tustin et al. (2005: 90) emphasise that qualitative research seeks to understand
a phenomenon through a less structured, more flexible approach. More often,
researchers try to explore motivations, and so the earlier term motivational
research. Qualitative researchers, such as Bashir, Afzal and Azeem (2008: 35),
and Cooper and White (2012: 7), agree that qualitative research focuses on the
description and understanding of a phenomenon or event being studied relating
to human experience.
According to Field (2009: 18), the quantitative research method entails the application of statistical models as well as the portrayal of trends through graphs. In
addition, in a quantitative study, numerical data are collected to answer a given
research question (Christensen et al., 2014: 46). Moreover, quantitative research
uses independent and dependent variables to establish relationships between
constructs (Nyamita, 2014: 113). Briefly, quantitative approaches focus on the
collection of quantitative variables, analysed using quantitative methods and
presented using graphs, tales and charts (Devlin, 2006: 53; Creswell, 2014: 4).
Researchers can also use mixed methods to conduct research. Creswell (2014:
9) describes the mixed method as the collection, analysis and integration of both
quantitative and qualitative methods. For the purpose of the current study, a
quantitative approach was adopted to assess factors that influence the debt
financing of MoEs within the control of the CoJ scientifically. This research
method was selected based on a number of factors, namely on the purpose of
the study, the variables under study and how data was analysed (Mogaladi, 2016:
35).
67
4.5
TARGET POPULATION AND SAMPLING
According to Tustin et al. (2005: 96), the group of people, entities or subjects from
whom data can be obtained defines the population. This study’s targeted
population comprised 13 MoEs from the CoJ listed as a State Owned Company
(SOC) over a period of five years from 2011 to 2015. The research used 5-years
information (2011 to 2015) to analyse MoE's debt tolerance for the CoJ after the
2010 South Africa Soccer World Cup. Secondarily, the scope was limited to five
years due to the lack of availability of accurate post-2015 records for some MoEs.
The Companies and Intellectual Property Commission (CIPC), Google and the
entities’ website were the main sources from which the audited annual financial
statement data was extracted. It is worth noting that the population frame data
excluded other municipal entities within Gauteng, the likes of the City of Tshwane,
Ekurhuleni, Mogale City, and Sedibeng.
4.5.1 Sample design
The sample size for the study comprised all 13 MoEs in the CoJ. There are two
sets of sampling methods, namely probability and non-probability sampling.
Tustin et al. (2005: 344) describe probability samples as the samples where all
elements within a population stand a chance of being selected, while in the nonprobability sampling the chances of selection from the population sample are
unknown. There are five types of non-probability sample methods, namely purposive, convenient, judgmental, quota and multiplicity sampling. For the purposes
of this study, a non-probability convenience sampling method was applied.
The following criteria were used to select the MoEs included in the final sample:
•
For an MoE to be elected, it had to be operating with the CoJ;
•
It had to be operational during the years under study; and
•
Its financial statements had to be available, or easily attainable.
In the event that financial information is unclear or not easily accessible, some
participants were consulted. The participants who assisted with information that
was not available are the chief financial officers or financial managers. These
participants were considered based on their seniority and their access to historic
financial information.
68
Upon the collection of the data, it was discovered that two entities (City Parks and
Johannesburg Zoo) had been merged into one. The total number of entities for
which financial statements were drawn was, therefore, 13 entities. Financial data
was available from 97% of the MoEs. The 3% non-collected information was
represented by an arithmetic mean, and the table below shows the collection rate.
Table 4.1: Financial statements data
CoJ MoEs
Annual financial statement collected
The annual financial statement not collected
Total number of annual financial statement
Total
%
63
97%
2
3%
65
100%
Source: Self-generated by researcher
4.6
DATA COLLECTION AND PROCEDURES
Primary data refers to data collected for a current specific research problem at
hand, using fit-for-purpose procedures for a specific research problem (Hox &
Boeije, 2005: 593). In addition, Brief (2012: 3) posits that primary data is generally
understood as data collected from the information source. In most cases, primary
data would not have undergone analysis before being included in the needs’
assessment. Furthermore, primary data are collected through personal interviews
or discussions with members of the affected community. They can also be
collected through interviews, radio communication, email exchange and direct
observation (Hox & Boeije, 2005: 593). Conversely, secondary data is data in a
quantitative format. Most of the time, the information consists of studied objects
whose characteristics are coded in variables that have a range of possible values
(Hox & Boeije, 2005: 593). Brief (2012: 3) noted that secondary data can be
collected by other researchers prior to inclusion in the current assessment. In
addition, this data can be extracted from sources, such as published research,
Internet material, media reports, and other sources (Brief, 2012: 3).
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4.6.1 Sources of secondary data
Secondary data was collected from annual financial statements and integrated
annual reports. This data was available using the entities’ website, CIPC, and
Google. The purpose of using such integrated secondary data sources was to
complement the collected information in the event that financial statements were
not precise. An ethical clearance was sent to all the entities detailing which
additional information was requested. In the event that information obtained was
either inadequate or unclear, some key personnel were consulted. These
participants’ identities, as well as those relating to their entities, were not
disclosed in the report. All the data that were collected were based on the
variables shown in Table 4.1 As was mentioned in earlier sections, primary data
was not considered because of the poor response from participants.
4.7
MEASUREMENT OF VARIABLES
In order to achieve the objectives of this study, the researcher had first to define
all the variables under study. The debt financing variable (financial leverage) is a
dependent variable. The primary leverage used in this study was book leverage.
According to Sibindi (2017: 113), book leverage measures the broader scale of
leverage. Nyamita (2014: 117) posits that book leverage is the proportion of the
entity’s debt finance to the book value of the assets of the entity. In contrast,
Nyamita (2014: 117) posits that market leverage, which is the proportion of the
entity debt market value of the entity, is a better option to depict leverage. The
study applied the book leverage as well as the financial leverage (summarised in
Table 4.2 below) as the proxies’ level of debt financing.
4.7.1 Measurement of the dependent variable
Table 4.2: Debt-financing (financial leverage) as a dependent variable using various proxies
Name of variable
Factor
Proxy used
Total debt leverage (TDL)
Internal
Total debt/Total assets
Long-term debt leverage (LDL)
Internal
Long-term debt/Total assets
Short-term debt leverage (SDL)
Internal
Short-term debt/Total assets
Source: Self-generated by researcher
70
Nyamita (2014) computed the variables in the above table based on the methodology as follows:
1. Total debt ratio =
Short-term debt + Long-term debt
Total assets
2. Long-term debt ratio =
Long-term debt
3. Short-term debt ratio =
Short-term debt
Total assets
Total assets
4.7.2 Measurement of explanatory (independent) variables
The explanatory (independent) variables used in estimating the equation in model
1 were outlines of how explanatory variables influenced debt financing in various
studies.
Table 4.3: Independent variables which include internal and macroeconomic factors
Name of variable
Internal/macroeconomic
factor
Method
Size of the entity
Internal
Natural logarithm of total assets
Age of the entity
Internal
Number of years since the
foundation
Profitability
Internal
Operating profit/Annual sales
Business risk
Internal
Total debt/Total equity
Corporate tax rate
Internal
Income tax charge/Profit before tax
Entity’s nature of assets
(tangibles)
Internal
Tangible assets/Total assets
Growth Opportunity
(entity)
Internal
Capital expenditure/Total assets
Inflation rate
Macroeconomic
Consumer price index
Economic Growth
Macroeconomic
Annual gross domestic product
Interest rate
Macroeconomic
Average interest rate
Source: Self-generated by researcher
71
4.7.2.1 Entity size
Over the past decades, researchers have used different principles as a measure
of entity size. Jardinee (2014: 33), Baltacı and Ayaydın (2014: 41), Nyamita
(2014: 118), and Sibindi (2017: 116) measured entity size as a natural logarithm
of either sales or total assets. In this study, however, total assets to measure the
size of the entity as per the methodology by Daskalakis and Psillaki (2008: 91),
Kasozi (2009: 69), and Mogaladi (2016: 18) divided tangible assets. The studies
above have conclusively shown that the size of the entity variable can be
measured by either the natural logarithm of sales, net sale, and by a logarithm of
gross income or total assets. Since there are many proxies, the study opted for a
natural logarithm of total assets to measure of the entity’s size variable.
4.7.2.2 Age of the entity
Abor (2007: 59), Luscombe (2009: 61) and Chenesai (2009: 18) argued that the
entity’s age has an influence on the entity’s debt financing. Abor (2007: 59) used
a panel regression model to establish a significant positive relationship between
age and long-debt, short-term and total debt. Regardless of other studies that
found this positive relationship, Elomo (2014: 11) argued that, having to look at
theories between the age of the entity and leverage, the relationship thereof could
be seen differently. The study explored the entity’s number of years to measure
the age of the entity.
4.7.2.3 Profitability
Different authors have always measured profitability differently. Deesomsak, et
al. (2004: 394) and Daskalakis and Psillaki (2008: 91) measured profitability as
operating profit divided by the total assets, while Luscombe (2009: 38) and
Mekonnen (2016: 98) measured profitability as earnings before interest and tax
(EBIT) divided by total assets. Nyamita (2014: 118) emphasises that profitability
should be measured as operating profit divided by annual sales while Sibindi
(2017: 117) argued that profitability should be determined by return on assets,
which is net income divided by total assets. It is, in light of these discrepancies,
that this study used the operating profit of the entity divided by annual sales as a
measure of profitability.
72
4.7.2.4 Business risk
In the review of empirical research, it was noted that most studies used standard
deviation to measure business risk. For instance, Deesomsak, et al. (2004: 394)
used the percentage of standard deviation in annual operating income scaled by
the value of total assets. In contrast, Luscombe (2009: 38) used the standard
deviation of sales over the total assets to measure the risk on debt of the
business. Conversely, Naidu (2011: 35) used the standard deviation of each
bank’s return on equity (ROE) as the proxy for business risk. On the other hand,
Nyamita (2014: 119) used the standard deviation of operating profit divided by
total assets to measure business risk. The study also acknowledges the different
proxies used by researchers. The study, however, used the debt-to-equity ratio
to measure business risk.
4.7.2.5 Corporate tax rate
According to Luscombe (2009: 45), corporate tax shield is one of the primary
advantages of debt financing. Specifically, the author noted that the corporate tax
rate of a country is largely influential in its entity’s optimal capital structure
(Luscombe, 2009: 45). In light of the above statement, researchers like Luscombe
(2008) and Nyamita (2014) used the corporate tax rate as a variable in their
studies. In a study by Abor (2008: 193), where the corporate tax was used as a
variable, a positive relationship was established between the corporate rate and
the short-term debt ratio while a significant negative relationship was established
between the corporate and debt-equity ratio, particularly in large entities.
Moreover, the study measured the corporate tax rate as the effective tax rate,
which is a ratio of current income tax charges and profit before tax. In instances
where an entity does not pay tax, a marginal tax rate was used as a proxy to
measure the corporate tax rate.
4.7.2.6 Assets tangibility
The debate around the measure of the asset tangibility of an entity has recently
escalated. This may be partly owing to the wide range of researchers like
Lubscombe (2008), Rajan and Zingales (1995), Kasozi (2009), and Elomo
(2014), who have measured asset tangibility in different ways.
73
Rajan and Zingales (1995: 1454) discovered that an asset's tangibility or the
collateral value of assets is a determinant of leverage. Rajan and Zingales (1995:
1451), Kasozi (2009: 71), and Elomo (2014: 21) measured the Collateral Value
of Assets (CVA) as the ratio of fixed assets to total assets. The authors found that
the variable is positively associated with leverage. On the contrary, Frank and
Goyal (2009: 9), Kayo and Kimura (2011: 360), Baltaci and Ayaydin (2014: 52),
and Nyamita (2014: 118) measured tangible assets divided by total assets to the
entity, which was the case in this study.
4.7.2.7 Growth opportunity (entity)
The study extends the factors that affect debt finance to macroeconomic factors
as was done by prior researchers such as Luscombe 2009, Elomo 2014, and
Nyamita 2014). Specifically, the study included the growth opportunity of an entity
as a factor affecting debt finance. According to Elomo (2014: 22), several
indicators are considered as the appropriate proxy to measure the entity’s growth
opportunity. In other studies, researchers like Rajan and Zingales (1995: 1451);
Kasozi (2009: 71) and Kayo and Kimura (2011: 360) used the ratio of the market
value of assets divided by the book value of assets as a proxy for growth/
investment opportunities. On the contrary, Titman and Wessels (1988: 4), Frank
and Goyal (2009: 9) and Nyamita (2014: 119) used the ratio of capital expenditure
over the total assets and percentage in changing the total assets. A study by
Baltaci and Ayaydin (2014: 52), and Bancel and Mittoo (2004: 122) used the
change in GDP and Price/Earnings (P/E) ratio to measure the entity’s growth. In
this study, capital expenditure was scaled by the value of total assets as a proxy
to measure the entity’s opportunity for growth.
4.7.2.8 Inflation and interest rate
A considerable amount of literature by authors like Manamela (2012), Stiglingh
(2015), Mashele (2008) and Malaza (2017) to mention only a few, has been
published on inflation and interest rates. These studies echo that macroeconomic
performance indicators of a country, like the inflation rate and the interest rate,
were used interchangeably as a macroeconomic factor. For instance, in a study
by Frank and Goyal (2009: 11) and Mokhova and Zinecker (2014; 534), an
expected inflation rate was used to measure the interest rate variable.
74
On the contrary, Deesomsak and Paudyal used the interest lending rate to
measure the interest rate variable. The current study used the consumer price
index (CPI) to measure inflation, while the study followed Nyamita (2014) where
the average interest interest rate was used measure interest rate .
4.7.2.9
Economic growth
Economic growth is a common variable that can influence debt, especially in
developing economies. Given the economic crunch, Stinglingh (2015: 30) posits that
the South African economy is in significant danger of failing to achieve consistently
high economic growth rates. To establish the effects of the economic growth variable
on the debt financing of MoE for the CoJ, the study used the yearly South African
GDP rate, sourced from the South African Reserved Bank fact sheet, to measure
economic growth. A recent study by Nyamita (2014: 120) used Kenya’s GDP figures
under Kenya’s economic status. On the contrary, De Jong, Kabir and Nguyen (2008:
1959) used the GDP figures from the World Bank database in their global study. In
this study, GDP figures for variables are listed in the figure below:
Figure 4.1:
Gross domestic product
Source: STATS SA, 2017
75
4.8
RESEARCH MODEL
4.8.1 Model for factors influencing debt financing
The study used longitudinal data, which are a combination of both cross sectional
and time series data. According to Cooper and Schindler (1998: 132), a study that
utilises cross-sectional data will have an observation of key variables at one point in
time. In corroborating this, Christensen et al. (2014: 66) argued that cross-sectional
study needs to be extracted from the targeted sample in a single and relatively short
period. It suffices, therefore, that a single or once-off period is extensive enough to
collect data from all the entities. Regarding longitudinal or panel data, Christensen et
al. (2014: 66) emphasise that data are collected at two or more (t) points within a
period of observation (time-series) for the same entity (n). Specifically, a data set of
a longitudinal study encompasses repeated observations of an outcome and a set of
covariates for each of the subjects under investigation (Scott et al., 1988: 121)
To estimate the equations and determine relationships amongst variables, the study
also used a panel regression model (multi-linear panel). According to Daskalakis and
Psillaki (2008: 91), panel regression models lower collinearity among the explanatory
variables, and so improve the efficiency of econometric estimates. A panel
regression model enabled the researcher to control unobservable and unmeasurable
variables (Torres-Reyna, 2007: 3). A number of studies made use of the multiplelinear panel-data regression model. These include Deesomsak, Paudyal and
Pescetto (2004: 392), Antoniou, Guney and Paudyal (2008: 69), and Nyamita (2014:
115). Briefly, the study employed the panel regression model as was done in the
above-mentioned studies.
Before modelling the equation, financial debt, also called leverage, was depicted as
a function of firm specific and macroeconomic factors as follows:
Financial leverage = ∫(∑ firm − specific factors + ∑ macroeconomic factors)
To determine whether there is a relationship between debt finance and interest rate,
profitability, size, corporate tax, age, growth, asset tangibility, and business risk, the
estimation equation was modelled as depicted in equation 1 below:
𝑁
𝑌𝑖𝑡 = α0 + ∑ 𝑘 = 1𝛽𝐾 𝑋𝑘𝑖𝑡 + 𝜀𝑖𝑡 + 𝜇𝑖𝑡
1
Where 𝑌𝑖𝑡 is a measure of debt financing (financial leverage) of entity 𝑖 in year t and
X represents the measure of the explanatory variables (entity’s specific factors and
76
macroeconomic factors), µ represents unobserved factors (either firm-specific or
macroeconomic) and α0 is the constant term. β forms K = 1 and includes k to N which
are unknown parameters to be estimated. The measure of explanatory variables X
includes k factors, which is the total number of all factors influencing debt financing
observed in this study.
The explanatory variables (independent) and their respective measures are given in
Table 4.3. The variables used in the study are divided into two, namely independent
and dependent variables. According to Johnson and Christensen (2018: 3),
“Independent variables (symbolized by “IV”) are the presumed cause of another
variable, while dependent variables (symbolized by “DV”) are the presumed effect or
outcome”. Simply put, dependent variables are influenced by one or more of the
independent variables (Christensen et al., 2014: 47). The estimation equation 1 was
modelled based on the secondary data from the annual financial statements and
reports. From there, the Panel EGLS (Period SUR)model was used to determine
relationships between debt financing and independent variables. Regression
analysis was complemented by the use of Spearman Correlation analysis to
determine associations between factors influencing debt and debt.
4.8.2 Model to analyse the different types of debt
To achieve objective number 2, which looks at an in-depth analysis of the various
types of debt, the study used descriptive statistics.
4.8.3 Model to determine the extent of debt financing
The study analysed the extent of debt financing substantively preferred by MoEs,
using descriptive statistics. The study analysed entries in the balance sheet using
the basic accounting equation 1 and aggregate cross-sectional measures for each
period to highlight the types of debt used and the extent of debt usage. In the same
vein, other researchers, like Frank and Goyal (2005: 155), Smith (2012: 150) and
Nyamita (2014: 114), used the same approach by analysing the common-size
balance sheet and the common-size cash levels to determine the extent of debt
financing within entities.
77
4.9
HYPOTHESES DEVELOPMENT
In light of the above discussion, the study formulated the following hypotheses
based on the causal variables to test their influence on debt financing. As a result,
the first point of departure was to test whether the casual variables have peculiar
attributes to influence debt financing.
4.9.1 Factors influencing debt financing
•
H1: Size of the entity influences debt financing positively
The above hypothesis was predicated from the premise of the trade-order
theory. Moreover, there is an plethora of empirical literature supporting the notion
that the size of an entity has a positive influence on debt financing (Antoniou et
al., 2008: 73; Luscombe, 2009: 46; Ahmed et al., 2010: 9; Lim, 2012: 197;
Bartoloni, 2013: 142); Nyamita, 2014: 123; and Sibindi, 2017: 103). On the
contrary, it is important to note that the pecking-order theory posits a different
view. According to Sibindi (2017: 103), the growth of an entity forces the entity
to generate more retained earnings, which enables it to fund more operational
costs out of retained earnings instead of debt. Despite this, this study expects
size to be positively associated with debt finance.
•
H2: Age of the entity influences debt financing positively
This hypothesis was based upon the notion that the principles of the pecking
order theory postulate a significant relationship between the age of the entity and
its debt. In terms of the hierarchical ladder of preferences, an entity uses retained
earnings prior to using its debt. It is hypothesised that bigger entities would have
established a good relationship with lenders while small entities are riskier in the
market (Luscombe, 2009: 49).
•
H3: Profitability of the entity influences debt financing negatively
Profitability was predicted to have a negative influence on debt financing as per
the predictions of the pecking order theory. Authors such as Bartoloni (2013),
Booth et al. (2001: 117), Rajan and Zingales (1995: 1457), Shyam-Sunder and
Myers (1991: 221), Nyamita (2014: 123), and Sibindi (2017: 101) have affirmed
this, among others. Contrary to this, the trade order theory predicts a positive
association between the entity’s profitability and debt financing.
78
•
H4: Business risk influences debt financing negatively
This hypothesis was formulated based upon the presupposition that different
factors can pose a significant risk to the entity. Business risks could be based on
both domestic and international monetary factors. This study predicted a
negative impact on debt financing, which is consistent with the logic of the tradeoff theory. In support of the above, a study by Luscombe (2009: 47) predicted a
negative impact on long- and short-term debt. Expect the relationship between
short-term debt and business risk can, however, to be low, particularly for an
entity that relies on short-term debt compared to one that relies on long-term
debt.
•
H5: Corporate tax influences debt financing negatively
Corporate tax is among the variables that are set to have a negative influence
on the debt financing of an entity. As such, this hypothesis tested how corporate
tax influences debt financing within the municipal entities. In this regard, the
study predicted a negative influence.
•
H6: Assets tangibility influences debt financing positively
Drawing from the above hypothesis, assets tangibility is one of the casual
variables, which can influence debt financing. Abor (2007: 173) posits that
previous studies appeared to propose that entities with a higher proportion of
fixed assets have the capacity to attract more debt. In terms of the trade-off
theory, a positive relationship is predicted between assets tangibility and
leverage while an inverse prediction is predicted by the pecking order theory. In
light of this study, a positive relationship was predicted between asset tangibility
and debt financing.
•
H7: The growth of the entity influences debt financing negatively
The above hypothesis is set to investigate whether the growth prospects of an
entity have an influence on debt financing. According to the trade-off theory,
bigger entities run a risk of bankruptcy if the operations are funded from debt. It
further projected that when it comes to debt financing, there is an optimum point
at which the benefit that is derived from the debt interest tax shield is maximum,
beyond which point it diminishes (Sibindi, 2017: 102). The pecking order theory
predicts contrary results. The hypothesis predicts a negative association
between growth and debt financing of the entity.
79
•
H8: Inflation rate influences debt financing negatively
and
•
H9: Interest rate influences debt financing negatively
Macroeconomic factors, such as inflation and interest rate, are casual variables,
which have an impact on an entity’s debt financing. The change in these
macroeconomic conditions may yield a positive or negative effect on debt
financing depending on the type of debt the entity acquires. Chisasa (2014: 31)
argues that economic stability and a conducive institutional framework are
perceived to be preconditions for the positive influence of finance on economic
growth. In light of the study, the above hypothesis predicted a negative influence
between inflation and interest rates on an entity’s debt financing.
•
H10: Gross domestic product influences debt financing positively
In trying to determine the role that the GDP plays in influencing debt financing,
the above hypothesis predicted a positive relationship between the GDP and
debt financing.
•
H11: Economic growth influences debt financing positively
The last hypothesis sought to examine whether economic growth influences debt
financing. To a larg extent, it could have been argued that economic growth
stimulates investment and job opportunities while the opposite applies. The
study has noted the importance of economic growth, particularly in developing
countries. The study, therefore, predicted a positive relationship between
economic growth and debt financing.
4.10
METHOD OF DATA ANALYSIS
Data analysis is described as a procedure of gathering, drawing and validating
conclusions derived from the data (Saunders et al., 2012: 669). As stated earlier, an
econometric Panel EGLS (Period SUR) regression technique was used and the
quantitative analysis of secondary data was the preferred method for this research..
To meet the objectives of the study, the researcher used EViews to analyse data by
means of panel estimation methods. In conjunction with the above, similar tools were
used to determine the source of debt financing substantively preferred by CoJ
municipal entities.
80
As mentioned before, secondary data was gathered from annual financial
statements to check whether a relationship exists between debt finance and a
number of explanatory variables. The data collected were analysed using the
EViews software package. Precisely, the variables discussed in earlier sections
of this chapter were imported from Microsoft Excel and estimated in EViews. The
diagnostics tests were carried out, the model was found to be appropriate, and
the results are presented in Chapter 5 through graphs and tables.
4.11
RELIABILITY AND VALIDITY
Babbie (2007: 143) identified reliability and validity as essential tools in research.
According to LoBiondo-Wood and Haber (2014: 290), reliability is the ability of a
research instrument to measure the attributes of variable or constructs consistently.
Similarly, Struwig and Stead (2013: 138) defined reliability as the degree to which
test scores are truthful, consistent or stable. The discussion suggests that reliability
is primarily concerned with the consistency of measures. Moreover, Nyamita (2014:
133) argued that, if the variables of a study are not reliable, it becomes more
challenging and ambiguous to test hypotheses or to make inferences about the
relations between variables in quantitative research. Since the study applied a
quantitative approach, reliable and objective annual financial statements were used
to obtain data. Furthermore, a pilot study was conducted as a pre-testing instrument
to improve the reliability of the study, using EViews.
LoBiondo-Wood and Haber (2014: 290) also defined validity as the extent to which
a research instrument measures the characteristics of the concept accurately. There
are two validity processes, namelyinternal and external validity. Polit and Hungler
(1993:17) defined internal validity as the degree to which the effects of a dependent
variable are a result of the independent variable and not emanating from peripheral
variables. In contrast, external validity can be defined as the degree to which research
findings can be generalised beyond the sample and setting of the study. To ensure
validity, the study used variables, which had been tested to measure the variables
under study commonly used by other researchers. Moreover, robustness tests and
the Arellano-Bond tests were applied to test the validity and reliability of the study.
81
4.12
DELIMITATIONS
This study specifically focused on the municipal entities of the CoJ. As indicated
earlier, all 13 entities were part of the sample study. The audited financial statements and information from the integrated annual report were used as a source
to extract reliable data to answer the research questions. The debt ratio was used
to determine the source of debt substantively preferred by these MoEs.
4.13
LIMITATIONS
The initial main objectives of the study sought to determine and establish factors
that influence the debt financing of MoEs. Despite the strength of the
methodologies used in the study, however, there are still some limitations. As
mentioned earlier, the study used two measuring instruments to make the results
valid and reliable as per the content and predictability of the research.
Questionnaires, which consisted of 13 questions, were sent to participants. The
response rate, however, was poor, making this a major limitation. The study,
therefore, was limited to data collected from the annual financial statements and
information from the integrated annual report, which produced a factual, valid and
reliable study.
4.14
ANONYMITY AND CONFIDENTIALITY
Apart from the information obtained from the annual financial statements, the
identity of participants from the lime questionnaire will be kept confidential, even
though such information was never used in the study. In addition, the researcher
upheld the anonymity of the participants who assisted in making the information
available in compliance with the POPI Act. The purpose of the POPI Act, No 4 of
2013 is to promote the protection of personal information by both public and
private bodies. It stipulates that all South African institutions conduct themselves
in a responsible manner when collecting, processing, storing, and sharing
another entity's personal information by holding them accountable should they
abuse or compromise personal information in any way. Finally, the data from the
study will be retained for 15 years for any further analysis and then disposed of
appropriately.
82
4.15
ETHICAL CONSIDERATIONS
The study was strictly conducted in accordance with the University of South
Africa’s (Unisa’s) Research and Ethics Policy. Prior to sending the request for
additional information that was not readily available on the public domain an
ethical clearance was obtained from the Unisa’s College of Accounting Sciences
Research Ethics Committee. A formal letter was sent to the MoEs requesting
permission to be furnished with some information that was not accessible on the
public domain. All information gathered with regard to this study was kept private
and confidential by making use of pseudonyms as prescribed by the POPI Act.
4.16
CONCLUSION
This chapter has detailed the methodology applied in this study. Quantitative
research methods were employed to gather and present data. The study also
used a combination of a descriptive and correlational research design to determine whether debt finance is related in any way to the various factors identified.
Furthermore, longitudinal research techniques were used to analyse the sampled
data for the five-year period of 2010–2015.
In addition, the chapter included a discussion on the research questions, objecttives and hypotheses. Section 4.7 detailed how the variables used in this study
were measured. From there, the ethical considerations, delimitations, limitations,
validity, and reliability issues were discussed. In conclusion, this chapter sets a
firm basis for the next chapter, which focuses on the presentation and analyses
of the findings of this study.
83
CHAPTER 5
DATA ANALYSIS AND DISCUSSION OF RESULTS
5.1
INTRODUCTION
The study is a quantitative research study using two components, namely descriptive
and inferential statistics. This chapter presents the results of the data analysis and
its findings. It further presents the results of testing the hypotheses developed in
Chapter 1 of the study. The results of the study are discussed under the key
subsections in line with the research objectives. In brief, the purpose of this chapter
is to present the results regarding factors that influence the debt of MoEs for the City
of Johannesburg. A statistical software, EViews (Econometric views), was used to
conduct data analyses based on the Panel EGLS (Period SUR) regression model.
The first section of this chapter deals with descriptive statistics (section 5.2) followed
by a correlation analysis (section 5.3) of the factors influencing debt financing of the
MoEs. Section 5.3 also covers the extent of debt substantively preferred by MoEs.
The rest of the chapter is organised as follows: section 5.4 looks at the results from
the panel regression model; and the chapter concludes with a summary of the
findings in relation to the objectives of the study.
5.2
DIFFERENT TYPES OF DEBT
The section below gives an overview of the interpretation and analysis of the results
on the different types of debt used the by various MoEs for the CoJ. The data
presented on the different tables below was extracted from MoEs of the CoJ for the
period between 2011–2015.
5.2.1 Different types of debt used by MoEs within the CoJ
Tables 5.1 and 5.2 below present evidence of the common practice by MoEs in terms
of the type of debt used within the CoJ. Table 5.2 presents the descriptive statistics
of the type of debt used by the 13 MoEs for the period 2011–2015. Subsequently, in
Table 5.2, the types of debt used by MoEs are presented and expressed as a
percentage and ranked from highest to lowest. Moreover, it is worth noting that the
data from both tables was extracted from the annual reports and annual financial
statements of the 13 MoEs.
84
Both tables below provide empirical evidence that trade and other payables
(89%) and short-term finance lease obligations (83%) are the common type of
debt used by MoEs within the CoJ. Table 5.1 shows a maximum for trade and
other payables and short-term finance lease to be 0.99 and 1.00 respectively.
This means that, during the period under review (2011–2015), the total trade and
other payables and short-term finance lease were not greater than 1.00. This
phenomenon might be a way of avoiding debt to exceed assets and equity, since
that consequently poses a serious risk of insolvency.
Table 5.1: Descriptive statistics of types of debt of MoEs for the CoJ
N
Mean
Median
Std.
Deviation
Skewness
Std. Error of Kurtosis
Skewness
Std. Error Minimum
Maximum
of
0,117 Kurtosis
0,586
0,00000000
1,00000000
47 0,27917514
0,20444695
0,28971794
1,022
0,297
10 0,00188652
0,00000000
0,00645022
4,161
0,297
18,100
0,586
0,00000000
0,03749296
54 0,10567211
0,00660466
0,25169491
2,772
0,297
6,571
0,586
0,00000000
1,00000000
58 0,55589715
0,63828945
0,31281017
-0,494
0,297
-1,048
0,586
0,00000000
0,99144199
32 0,02293144
0,00000000
0,04778583
3,587
0,297
15,836
0,586
0,00000000
0,28859676
49 0,02142684
0,01137604
0,02666381
1,905
0,297
4,016
0,586
0,00000000
0,12765921
1 0,00004126
0,00000000
0,00020501
5,549
0,297
32,753
0,586
0,00000000
0,00139771
18 0,01296955
0,00000000
0,05411860
5,353
0,297
28,442
0,586
0,00000000
0,32820249
31 0,29025026
0,00000000
0,37754077
0,945
0,297
-0,708
0,586
0,00000000
1,00000000
48 0,12668984
0,00701855
0,25025319
2,493
0,297
5,474
0,586
0,00000000
1,00000000
35 0,16929036
0,00689708
0,30658096
1,752
0,297
1,514
0,586
0,00000000
1,00000000
21 0,09852560
0,00000000
0,25179640
2,289
0,297
3,568
0,586
0,00000000
0,89424557
10 0,09946696
0,00000000
0,19847056
2,256
0,297
4,875
0,586
0,00000000
0,88652980
21 0,00497811
0,00000000
0,01766951
3,441
0,297
10,656
0,586
0,00000000
0,08308127
20 0,03979268
0,00000000
0,13960194
3,310
0,297
9,408
0,586
0,00000000
0,60639226
10 0,00399821
0,00000000
0,01541202
4,233
0,297
18,486
0,586
0,00000000
0,08989561
Short Loan from
Shareholder
Loans from Bank
Short Finance
Lease Obligations
Trade and Other
Payables
Tax & Vat Payable
Current Provision
Bankoverdraft
Current Differred
Income
Long Loans from
Shareholders
Long Finance Lease
Obligation
Employees
Retirement benefits
Obligation
Longterm Deffered
Income
Deffered tax
Consumer deposits
LongTerm Provision
Project Funds
payable
Source: Researcher (EViews statistical software: 2018)
85
Table 5.2: Types of debt between 2011 and 2015 expressed in percentages
Types of debt financing withing COJ
Number = 65
Ranking
% = 100
Trade and other payables
58
89
Short finance lease obligations
54
83
Current provision
49
75
Long finance lease obligation
48
74
Short loan from shareholder
47
72
Employees retirement benefits obligation
35
54
Taxation and VAT payable
32
49
Long loans from shareholders
31
48
Long-term deferred income
21
32
Consumer deposits
21
32
Long-term provision
20
31
Current deferred income
18
28
Project funds payable
10
15
Loans from bank
10
15
Deferred tax
10
15
1
2
Bank overdraft
Source: Researcher’s own compilation (2018)
Interestingly, current or short-term provision appears to be the third type of debt
used by MoEs, with a ranking score of 75%. This is an indication that most MoEs
modify their financial conditions and make provisions for the existing and future
obligations. Provisions are followed by long-term lease obligations and short-term
loans from shareholders at 74% and 72% respectively. Reliance on the two
sources of debt is significant, and it is an indication that most MoEs are
comfortable with financing their activities out of these two sources of funding. The
mean of the long-term lease obligation (0.1267) is below the short-term loan from
shareholder (0.2792). It could, therefore, be concluded that, on average, MoEs
accessed short-term loans from shareholders more often compared to long-term
lease obligations. In the same vein, Table 5.2 indicated that only 54% of the MoEs
within the CoJ used employee retirement benefits as another source of funding
during the period of study.
86
Despite the above-mentioned types of debt used by MoEs within the CoJ, other
sources of debt considered by MoEs include taxation and VAT payable (49%),
long-term loans from shareholders (48%), long-term differed income (32%),
consumer deposits (32%), long-term provisions (31%), current differed income
(28%) project funds payable (15%), loans from bank (15% ) and deferred tax
(15%). These sources of funding give a good picture that MoEs within the CoJ
had an opportunity explore different types of debt during the period under study
(2011–2015). Another interesting finding is that 2% of MoEs within the CoJ used
a bank overdraft as a form of debt. This may suggest that MoEs tend to use fewer
bank overdraft facilities compared to the other sources of debt within the CoJ.
This may be owing to the suggestion that MoEs are discouraged to operate from
overdraft business and operational model. Another reason could be that the
existing bureaucratic process set by the parent municipality prohibits or regulates
the process of short-term borrowings.
In summary, the findings presented in these two tables address one of the set
objectives of the study, which is to investigate the different types of debt used by
MoEs for the CoJ between a period 2011–2015. The results clearly identified
those types of debt used by MoEs in the City of Johannesburg for the period 2011
to 2015.
5.3
THE EXTENT OF DEBT SUBSTANTIVELY PREFERRED BY
MoEs FOR THE COJ
The section below presents a summary of the descriptive statistics from the
common-size statement of financial position. Table 5.3 below demonstrates the
extent of debt substantively preferred by MoEs in the CoJ. In fact, Table 5.3
further details the trends of such debt within the MoEs in the CoJ for the five years
period from 2011–2015.
87
Table 5.3: Descriptive statistics for items of common-size statement of financial position from
2011 to 2015
Source: Researcher (EViews statistical software: 2018)
As shown in the results of descriptive statistics in Table 5.3, the maximum value
of the total debt was 1.652, with a short-term debt of 1.333 and long-term debt of
1.102. Similarly, the maximum value of total assets ratio is 1.000, with current
assets and non-current assets of 0.991 and 0.959 respectively. The results above
imply that, during the period 2011–2015, some MoEs relied more on borrowings
than their total assets. This is consistent with the findings from a similar study
conducted in Kenya by Nyamita (2014: 149), who found that SoEs borrowed more
than their total assets that implies that some entities were insolvent and had a
high risk of being put under insolvency.
In comparison with the above explanation, the minimum value of total debt was
0.229, while that for short-term debt was 0.153 and long-term debt was 0.0. On
the other hand, the maximum value for total debt was 1.65. That of short term
debt was 1.33 while for long term debt it was 1.10. These results suggest that a
small number of MoEs relied on short-term borrowings, while there was less
commitment with regard to the long-term borrowings. The standard deviation for
short-term debt was 0.257 and 0.230 for long-term debt, while for total debt it was
0.309 during the five years period from 2011–2015. This implies that, during the
year of the study, the reliance on debt varied for most of the MoEs under study.
88
This is substantiated by the fact that, even though long-term debt has a maximum
of 1.00, most MoEs do not rely on long-term debt (as shown by a mean of 0.23,
a standard deviation of 0.23 and a median of 0.20 all clustered together).
Furthermore, the results showed a minimum negative total equity of 0.653 and a
maximum positive equity of 0.771, both of which are lower than the minimum and
maximum values for total debt, which are 0.229 and 1.653 respectively. This
suggests that MoEs within the CoJ during the year of the study preferred to use
debt as a first source of financing. The findings are in line with the pecking order
theory as postulated in the literature review by Myers (1984: 581), Arnold (2005:
536), Gangeni (2006: 12), Abor (2007: 31), and Elomo (2014: 11) that entities
prefer to make use of retained earnings, followed by debt and finally equity.
5.4
THE ACCEPTABLE LEVEL OF DEBT WITHIN MoEs FOR
COJ
Table 5.4 below presents a common size statement of financial position, where
the values are expressed as a percentage for a five-year period between 2011–
2015. The results in the table explain the trends and behaviour of debts when it
comes to MoEs borrowings more effectively. The results are also linked to the
third and fourth research objectives and research questions of the study.
Table 5.4: Common-size statements of financial position from 2011 to 2015
Average statement of financial position’s
item as a fraction of total assets
2011
2012
2013
2014
2015
Average
Current assets
0.5285
0.5549
0.5152
0.5759
0.5937
0.5531
Non-current assets
0.4715
0.4451
0.4875
0.4241
0.4063
0.4469
Total assets
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
Short-term debt
0.4461
0.4587
0.4882
0.4961
0.5803
0.4939
Long-term debt
0.2814
0.2603
0.2335
0.2123
0.1315
0.2238
Total liability
0.7275
0.7190
0.7216
0.7084
0.7117
0.7177
Equity
0.2725
0.2810
0.2784
0.2916
0.2883
0.2824
Total liability and equity
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
Source: Researcher’s own compilation (2018)
The table shows a consistent trend in terms of borrowings among MoEs. Precisely,
it suggests that during the years under study, total debt was stable at an average of
89
72%. The behaviour in borrowing is consistent with findings by researchers, namely
Lemmon et al. (2008: 1589), Frank and Goyal (2009: 156) and Nyamita (2014: 149)
who noted the stability in the total debt ratio. A comparison between short- and longterm debt ratios showed a consistent behaveiour with a slight increase of 58% in
2015 under short-term debt, while there was a slight decrease of 13% on the longterm debt ratio. As a result, comparing the two, short-term debt appears primarily to
be used by MoEs as opposed to long-term debt ratio.
It can be concluded that MoEs within the COJ compared to the long-debt ratio
preferred the short-term debt ratio. It is interesting to note that equity financing was
constant with an average of 28%. This confirms the results in Table 5.4 suggesting
that MoEs within the COJ preferred debt as a source financing. Based on the above
results and on empirical evidence from authors like Lemmon et al. (2008: 1589),
Frank and Goyal (2009: 156) and Nyamita (2014: 149), it is clear that the acceptable
level of short term debt can cluster around an average of 49% while that of long term
debt can be around 22%.
5.5
REGRESSION RESULTS ON FACTORS INFLUENCING
DEBT FINANCING
Table 5.5 below presents Panel EGLS (Period SUR) regression analysis of results
to determine whether the factors identified in the hypothesis section affect debt
finance for MoEs in the CoJ. As indicated above, in section 1.8.6, a panel regression
model was used to identify the relationship between debt financing and a number of
factors within MoEs for the COJ.
5.5.1 Regression analysis of descriptive statistics on factors
influencing debt financing
The aim of descriptive statistics is to summarise and describe the features of data
in a study (Christensen et al., 2014: 394). This section presents the descriptive
statistics of the dependent and independent variables considered in the study.
The descriptive statistics are presented in Table 5.5 below. The table includes
measures of central tendency, namely mean and median, standard deviation,
minimum, maximum total number of observations (N), skewness, and kurtosis.
Moreover, descriptive statistics provide information regarding potential outliers
and non-normality in the data. In dealing with the regression analysis process, all
90
outliers that could influence the results were eliminated through a data cleaning
process. According to Nyamita (2014: 162), the regression analysis process
requires data cleaning in order to ameliorate outliers that may influence results.
The paragraphs below Table 5.5 provide a detailed discussion on the descriptive
statistics provided in the table.
Table 5.5: Descriptive statistics
VARIABLES
N
TDA
SIZE
AGE
PROF
BR
TAX
TANG
GA
INF
INT
ENG
MEAN
MEDIAN
65
STANDARD
DEVIATION SKEWNESS KURTOSIS MINIMUM MAXIMUM
65
65
65
65
65
65
0,7250
0,6900
0,3066
0,614
0,335
0,2300
1,6500
18,1025
18,7300
2,3542
-0,919
0,066
12,1800
21,9100
16,4615
18,0000
4,6371
-0,430
-1,376
8,0000
23,0000
3,8914
3,4159
13,3547
-0,496
3,051
-44,8656
46,0000
2,3680
1,3088
22,6336
-3,098
25,301
-137,6238
83,6104
28,4245
28,0000
9,9228
-0,614
3,433
0,0000
57,0000
36,9848
27,0533
29,6342
0,181
-1,604
0,6622
86,0000
8,9105
5,3535
12,2724
3,044
11,587
0,0843
72,6794
5,5840
5,3200
0,4533
1,031
-0,485
5,1800
6,4100
9,1000
9,0000
0,4094
0,183
-0,693
8,5000
9,7500
2,2200
2,2000
0,6787
0,287
-0,924
1,3000
3,3000
Source: Researcher (EViews statistical software: 2018)
Table 5.6: Explanation of the variables in Table 5.5 above for ease of reference
Variable
Description
Method used
N
Number of observations
65
TDA
Total debt ratio
Total debt/Total assets
Size
Size of the entity
Natural logarithm of total assets
Age
Age of the entity
Number of years since the foundation
Prof
Profitability
Operating profit/Annual sales
BR
Business risk
Total debt/Total equity
Tax
Corporate tax
Income tax charge/Profit before tax
TANG
Entity’s nature of assets (tangibles)
Tangible assets/Total assets
GA
Growth opportunity (entity)
Capital expenditure/Total assets
INF
Inflation rate
Consumer price index (CPI)
INT
Interest rate
Average interest rate
ENG
Economic growth
Annual gross domestic product (GDP)
Source: Researcher (EViews statistical software: 2018)
91
According to Table 5.5, the total debt ratio of MoEs within the CoJ has a mean
value of 72.5% (0.7250) and a median value of 69% (0.6900). The leverage
variable ranges from a minimum of 23% (0.2300) to a maximum value of 165%
(1.6500), with the standard deviation of 30.7% (0.3066). The results indicated by
the mean value of 72.5% (0.7250) on the total debt ratio suggest that MoEs within
the CoJ are highly leveraged. As a result, this suggests that those entities are
incurring greater financial risk. In summary, the results indicate that MoEs within
the CoJ commonly use a greater portion of debt. The results from the table
resonate with other studies conducted both within and outside South Africa. In
fact, a study conducted by Elomo (2014) corroborated these findings when the
author found that start-up firms in South Africa are leveraged at 62.2% (0.622).
In addition, this study was based on a sample size of 32 JSE listed firms. Similarly, a study by Nyamita (2014) reported that state-owned cooperation in Kenya
are highly levered with an average leverage rate of 64.1% (0.641). The author
used a sample of 50 income generating state corporations in Kenya.
The descriptive statistics in Table 5.5 show how MoEs within the CoJ differ when
it comes to size, age, profitability, business risk, tax rate, asset tangibility, and
growth opportunity. In this regard, the summary of the statistics results show that
the size of the MoEs for the CoJ, in terms of the value of total assets, has a mean
value of 18.103 and median value of 18.730, Std. deviation of 2.354, and it ranges
from a minimum value of 12.180 to a maximum of 21.910. This suggests that the
average size of the entities is approximately more than 18 times the value of total
assets. Interestingly, the mean is lower than the median though the values are
close to each other. Moroever, the variable is slightly skewed to the left as
indicated by a skewness value of 0.919. According to Kim and White (2003: 4),
however, this value is not critical at all as thresholds for deviations from normality
should be between -1 and +1 or, conservatively, between -2 and +2. In terms of
age, the descriptive results show that the entities have a mean value of 16.463
and median of 18.000, with a Std. deviation of 4.637 and range from a minimum
value of 8.000 to a maximum value of 23.000. In a nutshell, this implies that the
average age of MoEs for the City of Johannesburg is approximately 16.5 years
and this is probably influenced by a few small entities as the median age is 18
years, indicating that 50% of the entities are younger than 18 years.
92
In terms of profitability, the results show a mean value of 3.891, a median of 3.416
and a Std. deviation of 13.355. It ranges from a minimum of 44.866 to a maximum
value of 46.000. This indicates that, on average, profitability in sales of MoEs for
the CoJ was 3.9% for the period between 2011–2015. The business risk of the
entities shows a mean of value 2.368, a median of 1.309 and a Std. deviation of
22.634. Moreover, the business risk ranges from a minimum range of 137.624 to
a maximum range of 83.610. This suggests that, on average, the business risk
on MoEs for the CoJ on total debt/total equity is 2.4%. On the other hand, the tax
rate has shown a mean of 28.425, median of 28.000 and a Std. deviation of 9.923.
It ranges from a minimum value of 0.000 to a maximum value of 57.00. The
results suggest that the average tax rate for entities is 28%, which ordinarily is in
accordance with the South African corporate tax rate during the period of the
study (2011–2015).
Assets tangibility has a mean value of 36.985, a median value of 27.053, a Std.
deviation of 29.634, and it ranges from a minimum of 0.6632 to a maximum of
86.000. This indicates that, on average, tangible fixed assets account for 36.99%
of total assets. Furthermore, the variable opportunity growth has a mean of 8.911,
a median of 5.354 and a Std. deviation of 12.272. The variable also ranges from
0.084 to 72.680. The results imply that the MoEs for the CoJ have an average of
8.91% for fixed assets. Inflation and interest rates both have a mean of 5.584 and
9.100, with a median of 5.320 and 9.00 respectively. They also have a standard
deviation of 0.453 and 0.409, with a range from a minimum of 5.180 and 8.500 to
a maximum of 6.410 and 9.750. In a nutshell, inflation and interest rate are on an
average of 5.6% and 9.1% respectively. The last independent variable is
economic growth. The table shows the average of 2.220, a median of 2.200 and
a standard deviation of 0.679. It also has a minimum range of 1.300 to a maximum
of 3.300. This implies that average growth on MoEs is 2.2%, which is within the
South African economic growth for the period of the study (2011–2015). It is also
very clear that the skewness and kurtosis values of all the variables, for the
variables BR and GA where logarithm transformation was applied, were between
the threshold of +2 and -2, which was used to indicate the normal distribution for
these variables.
93
5.5.2 Results from Spearman correlation analysis
Spearman’s correlation analysis was conducted to determine the factors that can
influence debt finance either positively or negatively. Saunders et al. (2012: 478)
describe correlation analysis as a tool used to determine change; one variable is
accompanied by a change in another. As indicated before, the main objective of
this study has been to determine factors influencing debt financing of MoEs. As
a first step, Table 5.7 presents the Pearson correlation coefficients Sig (2-tailed)
for the variables used in the study. The Pearson correlation coefficient analysis
is robust against violations of normality and, therefore, applies to all the study
variables (Havlicek & Peterson, 1976).
The main aim of the correlation analysis was to understand the strength and
direction of the relationships between each pair of variables. While conducting
the analysis, however, the researcher had to ensure that there was no multicollinearity. Multicollinearity exists when two or more of the predictors in a regression model are highly correlated, so inflating the standard errors of the
analysis leading to incorrect inferences. Generally, absolute correlation values
above 0.7 can indicate potential multicollinearity. The results in Table 5.7 show
that the inflation rate could be a potential independent variable that could be
excluded owing to its high correlation values with the interest rate (.857) and
economic growth (.874). Whereas the results suggest that inflation rate and
interest rate as independent variables might be highly correlated. It is worth noting
that these variables did not use a common proxy i.e. nominal interest rate. In that
regard, it could be concluded that no problem of perfect multicollinearity was
created.
On the other hand, if multicollinearity was found, the inflation rate would have
been excluded from the model due to the problem of perfect multicollinearity. In
the study, to obtain the regression results, variables with the highest probability
values were dropped until the adjusted r-square value was at its optimal value,
including inflation rate. Table 5.9 therefore presents the relevant independent
variables (INT, PROF, SIZE, TAX, AGE and BR) as the factors that had an impact
on the outcome of the panel EGLS (Period SUR) model. The exclusion of such
variables could not have had an impact on the results and as proven before by Li
et al. 2014 who experienced similar challenges.
94
Table 5.7:
Correlations analysis
Source: Researcher (EViews statistical software: 2018)
Source: Timothy Gehle, Oct 2013, Laerd Statistics
The results of the correlation coefficients analysis demonstrate that the total debt
ratio (TDA) is positively related to size as indicated by a positive coefficient of
0.347. The relationship is, however, not strong since the coefficient is almost
close to 0. Moreover, GA was found to influence debt negatively and this is shown
by a coefficient of -0.466. It is also worth noting that this association is moderately
strong since the value is below 0.5.
95
There is a positive association between age and TDA as shown by a correlation
coefficient of 0.129, positive. The association is weak, however, since the value
is close to 0.1. This outcome mirrors that by Dang (2013: 176), Bassey, Arene
and Okpukpara (2014: 44), Baltacı and Ayaydın (2014: 50), and Nyamita (2014:
166) who noted that size and age are indications of trade-off and agency theory.
This implies that large and old MoEs tend to use more debt to finance their
activities.
On the other hand, the results above show that Profitability (PROF, -0.242) and
Growth Opportunity (GA, -.466) variables are negatively related to the total debt
ratio (TDA). The Growth opportunity’s (GA, -.466) association to debt ratio (TDA)
is negative but of medium/moderate strength, while the Profitability (PROF, 0.242) association to debt ratio (TDA) is small/weak, in a negative direction and
not statistically significant at the 5% level of significance, although statistically
significant at the 10% level of significance . In support, Nyamita (2014: 166) found
a negative correlation between Profitability (PROF) and total debt ratio (TDA).
The results of the study suggested that profitable and growing MoEs tend to use
less debt to finance their activities. The findings in a study by Elomo (2014: 28),
however, discovered that variables, such as profitability (PROF) and growth
opportunity (GA), within private entities in South Africa are positively correlated
to total debt, given the fact that there are different results in relation to profitability.
Other researchers using data from other MoEs apart from Johannesburg can
further investigate this concept.
The results of the correlation coefficient analysis also demonstrate that variables,
like tangibility (TANG) (-0.041), business risk (BR) (-0.026), inflation rate (INF) (0.036), and interest rate (INT) (-0.018), are negatively related to debt. Their
association is, however, weak considering that the coefficients are very close to
-0.1. The p-values also indicate that the association of these variables with total
debt e is of no statistical significance. The results on effective tax rate and
economic growth corroborate Nyamita’s (2014: 166) findings where total debt
leverage (TDL) was found to be positively and significantly related with several
measures of factors influencing debt financing, such as interest rate (INT) and
gross domestic product (GDP).
96
In terms of the study, GDP was used to measure economic growth (EGN) which
was found to be positively related to total debt (TDA). In conclusion, the results
above demonstrated that size and growth opportunity significantly influence total
debt. The section below shows results from the regression analysis carried out to
determine whether a relationship exists between the debt finance as a dependent
variable and a number of independent firm and macro-specific factors.
5.6
PANEL DATA
5.6.1 Panel data regression results
Chisasa (2014: 62) describes panel data as a combination of both cross-section
and time series data. When dealing with panel data, the same cross-sectional
unit is surveyed over time. In short, panel data consists of both space and time
dimensions and regression models based on such data are known as panel data
regression models. This section presents the results of the panel data regression
modelling. The Panel data regressions (EGLS (Period SUR) analysis was used
to identify the factors influencing debt financing within the CoJ. This was done by
determining the nature of the relationship between debt and various factors.
Moreover, the significance of such relationships was determined at 5% in
accordance with previous researchers such as Nyamita (2014), Feragen (2014),
Ebiringa et al. (2014) and Masocha (2017), to mention only a few. This implies
that if the p-value is >0.05, the result is statistically insignificant; on the other
hand, if the p-value is <0.05, it is statistically significant. The model includes all
the independent variables identified in the earlier chapters of this study.
The multiple regression results on Table 5.8 presents results that included all the
variables of interest and established the adequacy of the model and the statistical
significance of each variable in the context of the complete set of variables
considered. The observations consisted of 65 with-in a period of five years (2011–
2015), while the cross-section included was 13 and the method applied was
EViews statistical software.
97
Table 5.8:
Panel data regressions
Dependent Variable: TDA
Method: Panel Leas t Squares
Date: 11/20/18 Tim e: 10:37
Sam ple: 2011 2015
Periods included: 5
Cros s -s ections included: 13
Total panel (balanced) obs ervations : 65
Variable
Coefficient
Std. Error
t-Statis tic
C
ENG
GA
INT
BR
PROF
SIZE
TANG
TAX
AGE
0.182432
0.024881
-0.010749
-0.026488
-0.000494
-0.006317
0.039318
-0.000947
0.001392
0.008114
1.018209
0.062033
0.002725
0.100781
0.001618
0.002484
0.014180
0.001433
0.003317
0.009085
0.179170
0.401086
-3.944323
-0.262831
-0.305350
-2.542696
2.772684
-0.660782
0.419763
0.893113
R-s quared
Adjus ted R-s quared
S.E. of regres s ion
Sum s quared res id
Log likelihood
F-s tatis tic
Prob(F-s tatis tic)
0.400170
0.302016
0.256165
3.609134
1.723872
4.076956
0.000475
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Wats on s tat
Prob.
0.8585
0.6899
0.0002
0.7937
0.7613
0.0138
0.0076
0.5115
0.6763
0.3757
0.724967
0.306618
0.254650
0.589171
0.386640
0.650151
Source: Researcher (EViews statistical software: 2018)
In order to obtain the regression result in Table 5.9, it was considered important to
establish, in addition to the above result (Table 5.8), a set of predictors (independent
variables) that would maximize the adjusted R square value. The resulting optimal
regression model, provided a set of predictors (independent variables) that
maximized the proportion of variance in the dependent variable that could be
predicted by the resulting set of variables. In generating the final results, variables
with the highest probability value (i.e. insignificant) were discarded one by one and
the regression model was re-run with the remaining variables until the adjusted rsquare value was at its optimal value. The identification of the optimal value was
accomplished when the optimal value for the adjusted r-square value was obtained.
Regression results in Table 5.9 below include final independent variables (INT,
PROF, SIZE, EG, AGE and BR) which have an impact on the outcome of the optimal
data regression model. The adequacy of the model is measured by the adjusted R²
value, which shows the percentage of variance in the dependent variable explained
by the set of independent variables.
98
Table 5.9: Optimal panel data regression
Dependent Variable: TDA
Method: Panel Least Squares
Date: 11/27/19 Time: 21:54
Sample: 2011 2015
Periods included: 5
Cross-sections included: 13
Total panel (balanced) observations: 65
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
PROF
SIZE
AGE
LBR_1
EG
INT
-0.665310
-0.005525
0.025891
0.016900
0.142085
0.063147
0.042928
0.376627
0.002171
0.012130
0.006243
0.022382
0.036515
0.026269
-1.766494
-2.544346
2.134454
2.706897
6.348082
1.729343
1.634179
0.0826
0.0136
0.0370
0.0089
0.0000
0.0891
0.1076
Root MSE
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
0.202021
0.724967
0.306618
-0.145505
0.088660
-0.053112
1.220234
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.559108
0.513499
0.213865
2.652813
11.72892
12.25860
0.000000
Source: Researcher (EViews statistical software: 2019)
As indicated above, the process to arrive at the final optimal panel data
regression on table 5.9, started with the selection of the variable with the highest
significance value above 0.1 the variable selected were deleted from the analysis
and the regression model calculated. The process stop when the deletion of an
additional variable, cause no, or a decrease, in the adjusted R square value.
Table 5.9 therefore presents the relevant independent variables as the factors
that were included in the optimal regression model. Howeever, Table 5.9 does
not address the possibility of serial interaction and heteroscedasticity. As a result,
Period SUR (PCSE) standard errors & covariance was run to address serial
interaction and heteroscedasticity.
The data in Table 5.10 doesn not only analyzes the findings, but also discusses
and addresses any potential serial interference and heteroscedasticity. It is worth
noting that there are a number of models used to tackle autocorrelation and
heteroscedasticity, i.e. Prais-Winsten, Durbin Watson, Arellano-Bond AR (1) and
AR (2) p-tests, Bruesh-Godfrey, just to mention a few.
99
However, the most widely discussed approaches, the Cochrane-Orcutt, PraisWinsten, Hatanaka, and Hildreth-Lu procedures, are multi-step approaches
designed so that estimation can be performed using standard linear regression.
These approaches proceed by obtaining an initial consistent estimate of the AR
coefficients and then by estimating the remaining coefficients through a secondstage linear regression. Thus, all these approaches suffer from important drawbacks
which occur when working with models containing lagged dependent variables as
regressors, or models using higher-order AR specifications (In the same vein, there
is no clear serial correlation (MacKinnon & Davidson (1993) and Greene (2008). The
rule of thumb is that test statistic values in the range of 1.5 to 2.5 are relatively normal.
Therefore, there is no cause for concern in results table 5.10 about serial interaction
and heteroscedasticity because Period SUR (PCSE) standard errors & covariance
was introduced to deal with any possible autocorrelation and heteroscedasticity.
Table 5.10: Panel data EGLS (Period SUR)
Dependent Variable: TDA
Method: Panel EGLS (Period SUR)
Date: 11/27/19 Time: 22:00
Sample: 2011 2015
Periods included: 5
Cross-sections included: 13
Total panel (balanced) observations: 65
Linear estimation after one-step weighting matrix
Period SUR (PCSE) standard errors & covariance (d.f. corrected)
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
PROF
SIZE
AGE
LBR_1
EG
INT
-0.151930
-0.003547
0.015681
0.008758
0.147954
0.053154
0.023255
0.341482
0.001130
0.008494
0.005027
0.015585
0.023138
0.023686
-0.444914
-3.139223
1.846053
1.742232
9.493612
2.297232
0.981785
0.6580
0.0027
0.0700
0.0868
0.0000
0.0252
0.3303
Weighted Statistics
Root MSE
Mean dependent var
S.D. dependent var
Sum squared resid
Durbin-Watson stat
0.958310
3.834926
2.623073
59.69327
1.909236
R-squared
Adjusted R-squared
S.E. of regression
F-statistic
Prob(F-statistic)
0.738048
0.710949
1.014492
27.23572
0.000000
Unweighted Statistics
R-squared
Sum squared resid
0.531991
2.815974
Source: Researcher (EViews statistical software: 2019)
100
Mean dependent var
Durbin-Watson stat
0.724967
1.076358
The Panel EGLS (Period SUR)s model analysis shows a weighted statistics r² (rsquared) value of 74% (0.7380) and unweighted statisticsn r² (r-squared) value
of 72% (0.7249). The weighted statistics shall therefore be used for the purpose
of the results of the study for the interpretation of the r-square. The change in the
dependent variable (debt), therefore, is largely explained by change in the
independent variables. Clearly, this suggests that changes in debt are perfectly
explained by changes in independent variables, such as interest rate (INT),
profitability (PROF), size, economic growth (EG), age and business risk (BR).
5.6.2 Empirical results entity specific factors
Table 5.11 below gives an interpretation and summary of the results from panel
data EGLS (Period SUR) in Table 5.10. It shows the factors influencing debt
financing of MoEs within the CoJ, and those factors include interest rate (INT),
profitability (PROF), size (SIZE), economic growth (EG), age of the entity (AGE)
and BR (Business risk). It is important to mention, however, that not all independent variables contributed to the strength of the model. As a result, such
variables were automatically eliminated. The discussion that follows Table 5.11
affirms or rejects what has been the predicted in the hypotheses section in
Chapter 1. Before discussing the regression results, it is important to take note of
the predicted association between debt and the various independent variables,
based either on theory or on empirical evidence.
Table 5.11: Hypothesis predictions vs outcomes
Predicted
Outcomes
Effects
Effects
Association
Variable
Interest rate
Negative
Positive
Insignificant
Profitability
Negative
Negative
Significant
Size
Positive
Positive
Insignificant
Economic growth
Positive
Positive
Significant
Age
Positive
Positive
Insignificant
Business risk (BR/LBR_1)
Positive
Positive
Significant
Source: Researcher’s own compilation (2018)
101
•
Testing hypothesis H1: Interest rate influences debt financing negatively
The predictions in Table 5.10 show that a positive relationship between interest rate and debt. This is confirmed by the regression results showing a
positive coefficient of 0.0233. Interest rate was, therefore, found to be
positively related to debt. Despite this positive association, a p-value 0.3303
is indicative of a insignificant relationship since an acceptable p-value of 0.05
is considered for the purposes of this study. These results imply that interest
rates in South Africa influence the debt of MoEs positively. In other words, an
increase in interest rates cause higher borrowing costs, consequently, they
would less demands on goods and services because less spending.
The results of the study resonated with the findings by other researchers such
as Mekonnen (2016: 133) and Lemma, and Negash (2013: 1104) that found
a positive relationship between interest rate and debt. This, however, is in
contrast to results from studies conducted by Huang & Song, 2006: 32;
(Antoniou, Guney & Paudyal, 2008: 73; De Jong, Kabir & Nguyen, 2008:
1961; Jõeveer, 2013: 306; Foster & Young, 2013: 6; Nyamita, 2014: 175),
who found a negative relationship between interest rate and debt financing.
Suffice it to say that previous researchers have given us inconsistent results
based on different models. In conclusion, the results from this study rejects
the predicted null hypothesis on section 1.8 in Chapter 1 that says that
interest rate influences debt negatively.
•
Testing hypothesis H2: Profitability influences debt financing negatively
The results in Table 5.10 show that profitability, measured by operating profit
as a ratio of annual sales, negatively influences the debt of MoEs within the
City of Johannesburg. This is shown by the negative coefficient of -0.0035. The
relationship is, however, significant as showned by a p-value of 0.0027.
These results affirmed the findings by several researchers, such as De Jong,
Kabir and Nguyen (2008: 1961), Luscombe (2009: 59), Smith (2012: 157),
Jõeveer (2013: 306), Nyamita (2014: 171) and Mekonnen (2016: 133) who
found profitability to have a negative influence on debt.
102
These results, together with those mentioned above, are in accordance with
the predictions of the pecking order theory. The pecking order theory presumes that entities with higher profitability prefer to use internal financing
before opting for debt, so a negative relationship is expected between profitability and debt (Baltacı and Ayaydın, 2014: 49). Briefly, the results of the
study suggest that profitable MoEs use less debt in financing their activities.
This could be a sign that the powers of management and the boards of MoEs
in terms of debt-related decisions are limited. As a result, they tend firstly to
utilise profits before borrowing funds. In conclusion, one can say that the
results confirm the predicted hypothesis in Chapter 1, and they align to the
foundations laid by the pecking order theory.
•
Testing hypothesis H3: Size influences debt financing positively
The results are indicative of a positive relationship between the size of an
entity and debt, as shown by a coefficient of 0.0157. Moreover, the relationship is statistically insignificant with a p-value of 0.0700. It is important to
note that the outcome of these tests is consistent with the findings by other
researchers, such as Abor (2007: 120) and Mkhawane (2010: 48), who found
a positive relationship between size and debt. In support of a positive relationship between size and debt, the results are consistent with both trade
order and pecking order theories, both of which predict that large entities
should be highly leveraged compared to small entities (Sibindi 2017: 150). In
the context of MoEs, their results suggest that, when the entities grow, they
require more financial resources and they stand to benefit from an interest
tax shield. Moreover, the results suggest that MoEs require more debt only
when they grow. As a result, they tend to depend more on formal finance as
opposed to informal sources of finance, such as provisions, which tend to be
inadequate (Abor 2007: 120).
In contrast, there are couple of researchers who have observed that a
negative relationship exists between size and debt. Among those are Rajan
and Zingales (1995: 1451), Titman and Wessels (1988: 14), Nyamita (2014:
173), Baltacı and Ayaydın (2014: 49). The findings of the above researchers
resonate within the principles of pecking order theory.
103
Baltacı and Ayaydın (2014: 49) have indicated that the pecking order theory
of debt financing often assumes that bigger, more diversified state-owned
companies will use less debt and thus expect the size of the company to be
negatively linked to debt financing. In conclusion, the contrary outcome of the
hypothesis suggests that, in the context of the City of Johannesburg, the
MoEs do not follow the principle of pecking order theory. Size, therefore, has
a positive influence on the debt of MoEs within the CoJ and the results
confirm and support the predicted hypothesis in section 1.8.
•
Testing hypothesis H4: Economic growth influences debt financing positively
The empirical results on Table 5.10 show a positive association between
economic growth and debts with a coefficient of 0.0532. This relationship is,
however, significant as shown by a p-value of 0.0252. The results are
consistent and affirm the predicted hypotheses as indicated in Table 5.11.
The findings are similar to Alfaro et al. (2004) and Choong et al. (2010: 108)
that says economic growth affects the debt financing positively. This means
that growth in the economy allows the company to borrow less, hence,
Choong et al. (2010: 108) believe that in a developed economies with
advanced capital markets, private debt such as bank loans improves the
country’s economic growth, hence private debt, like bank loans, is still
common amongst European state-owned corporations.
In contrary, Agbloyor et al. (2014: 138) posits that in decaying private capital
flows into its component parts, factors such as private debt, foreign equity
portfolio investment and foreign direct investment, have a negative impact on
economic growth. Thus, economic growth result into a decrease in debt
financing while it increases the equity financing Boyd and Smith (1998: 552).
Although Agbloyor et al. (2014: 148) argued that economic growth could have
a negative impact on debt financing. However, that cannot be generalized
because most of the researchers mentioned earlier confirm the outcome of
these results that there is a positive relationship between economic growth
and debt financing. Such positive effects on debt financing indicate that more
efficient positive economic growth results in more prudent lending and
consequently the results affirmed the predicted hypotheses.
104
Testing hypothesis H5: Age influences debt financing positively
The age of the entity is one of the independent variables that was hypothesised
to have a positive and insignificant relationship with the debt of MoEs. The
variable was measured as the number of years the MoE has been in existence
since its establishment. The variable was set or estimated to have a positive
relationship with debt. The panel regression results in Table 5.9 show that age
has a positive (0.0088) association with debt and that the association is
insignificant at 5% (0.0868). The results align with the prediction of the null
hypothesis set in section 1.8 of Chapter 1. This also affirms the findings by
Abor (2007: 59) who indicated that the age of the firm is significantly and
positively related to total debt ratios. Firms that are operational for longer,
therefore, have a better opportunity to access the debt provided compared to
newly established firms.
On the other hand, previous researchers, such as Luscombe (2009: 62),
Smith (2012: 155), Nyamita (2014: 176) and Bassey et al., (2014: 44) observed a negative relationship between age and debt. Their observation is
aligned with the principles of the pecking order theory, where a negative
relationship between age and debt is recognised. According to the pecking
order theory, older entities with higher retained earnings follow the hierarchical
ladder, where retained income is used before incurring debt (Luscombe,
2009: 49). In conclusion, even though there is a disagreement among researchers, the results from this study suggest that MoEs within the CoJ do
not conform to the pecking order theory.
•
Testing hypothesis H6: Business risk influences debt financing positively
As is shown in Table 5.9, business risk is also one of the specific factors that
influence debt within MoEs in the CoJ. The results indicate that there is a
significant positive relationship between business risk and debt, indicated by the
positive coefficient of 0.14795 and a p-value of 0.0000. Gaud et al. (2005: 63),
Foster and Young (2013: 7), Lemma and Negash (2013: 1109), and Sibindi
(2017: 151) affirm this outcome, to mention only a few.
105
Moreover, this is in line with the pecking order theory discussed in the
literature review. Briefly, the results imply that MoEs with more debt tend to
have high business risks.
Contrary to the above results, some researchers found a negative relationship between business risk and debt (Frank & Goyal, 2003: 3; Deesomsak,
Paudyal & Pescetto, 2004: 398; Antoniou, Guney & Paudyal, 2008: 77; De
Jong, Kabir & Nguyen, 2008: 1961; Frank & Goyal, 2009: 32; Lim, 2012: 197;
Baltacı and Ayaydın, 2014: 54; Nyamita, 2014: 174). Even though most of
these results based on an outcome model noted a negative relationship
between business risk and debt, this cannot be generalised among entities
in developing countries
5.7
CONCLUSION
This chapter has provided, and discussed, the empirical results of the study in
line with the research objectives. In analysing the results of the factors influencing
debt financing of MoEs for the CoJ, the study first presented the descriptive
statistics followed by both Spearman correlation and Panel Regression analysis.
An optimal panel regression model was also used to test factors influencing debt
financing. Looking at the outcomes, the results of the model concluded that
independent variables, such interest rate, profitability size, economic growth and
age, are some of the major factors that influence the debt of MoEs within the CoJ.
The optimal regression analysis, however, focused at 74% on the variation in
debt, which was explained by the change in independent variables.
The results of the test hypothesis showed that profitability is negatively related to
debt. On the other hand, interest rate, size, economic growth , business risk, and
age were found to be positively associated with debt. The results of the
regression model, therefore, confirmed the set’s null hypothesis that independent
variables, such as age, economic growth, business, risk and size, have a positive,
whereas profitability has a negative relationship with debt financing. In fact,
interestingly, the only null hypothesis that was rejected was one that tested the
relationship between interest rate and debt. The next chapter will give a summary
of the entire results discussed above and present the findings as well as providing
some recommendations.
106
CHAPTER 6
CONCLUSIONS AND RECOMMENDATIONS
6.1
INTRODUCTION
The main objective of this study was to establish the factors that influence the
debt financing of municipal-owned entities (MoEs) within the City of Johannesburg (CoJ). In order to achieve this objective, three additional objectives were
also set, namely to analyse the different types of debt financing used by the
various MoEs for the MoEs, to determine the extent of debt financing substantively preferred by MoEs in the CoJ, and to establish the acceptable level of
debt financing for MoEs for the CoJ. The chapter summarises the findings and
interpretation of the results presented in the previous chapter. It further provides
the key findings, conclusion, recommendations, implications, the limitations of the
study, and recommendations for areas of further research.
This chapter is organised as follows:
•
Section 6.2 is the summary of the research findings of the study;
•
Section 6.3 details the key findings;
•
Section 6.4 outlines the contribution of the study to literature;
•
Section 6.5 discusses some of the limitations of the study;
•
Section 6.6 gives recommendations based on the results obtained from the
study; and
•
Section 6.7 outlines suggestions for future research; and section 6.8 concludes the chapter.
6.2
SUMMARY OF THE STUDY
In answering the research objectives of the study, certain factors were identified
using past empirical research. These factors include size, age, profitability, business risk, the corporate tax rate, the nature of assets (tangibles), growth opportunity (entity), the inflation rate, economic growth, and the interest rate. The study
employed a quantitative approach, using both descriptive and correlational
research designs.
107
Longitudinal data was collected from a sample of 13 MoEs from the CoJ.
Secondary data was extracted from annual financial statements of the entities
concerned for the period 2011–2015. To interpret and analyse the data,
Spearman correlation and Panel EGLS (Period SUR)regression analysis was
run. The results from these analysis tools were presented and interpreted. A
summary and detailed discussion of these results follows in the sections below.
As mentioned above, the objective of the study was to establish the factors that
influenced the debt financing of MoEs for the CoJ. This was done by determining
whether a number of identified factors are related with debt. The study is
premised on the basis that the extent of borrowings for MoEs are confined within
the legislative framework prescribed in the Municipal Finance Management Act,
no 56 of 2003 of National Treasury. As a result, the prescript of the legislative
frameworks makes it complex for MoEs to engage in borrowings without the
approval of the parent municipality because, in most cases, the debt financing of
MoEs has a direct impact on the finances of the parent municipality. In the past,
similar studies have been conducted by various researchers, namely Foster and
Young (2013), Nyamita (2014), Sebapadi (2016), Sibindi (2017) and Malaza
(2017) using data from developed and developing countries. It is on that basis
that the study attempted to narrow the gap in the literature by using data from a
developing economy such as South Africa.
6.3
FINDINGS REGARDING THE OBJECTIVES OF THE STUDY
The findings are presented in terms of the objectives of the study, which are listed
below:
•
To analyse the different types of debt financing used by the various MoEs for the
CoJ;.
•
To determine the extent of debt financing substantively preferred by MoEs in the
CoJ;
•
To establish the acceptable level of debt financing for MoEs for CoJ; and
•
To identify the factors that influence debt financing within the MoEs in the CoJ.
The results can be summarised as follows.
108
6.3.1 Different types of debt financing used by MoEs within the CoJ
As was seen in the previous chapter, most entities prefer to use debt financing
before opting for equity finance. This objective was set, based on Myers (1984:
16) who argues that, in terms of the pecking order theory, entities prefer to use
debt before opting for equity financing. In comparison, none of the similar studies
had been conducted on MoEs particularly within the CoJ metropolitan municipality. This study, therefore, tried to close this gap. A study by Nyamita (2014:
190) found that the use of debt by entities varies between private and public
entities in developed and developing countries. According to the empirical literature by Benmelech et al. (2013; 485) and Chen et al. (2013; 4), debt financing
models are classified according to long and short-term debt as well as public and
private debt. Furthermore, studies by Adamolekum (1999: 42–43) and Nyamita
(2014: 191) found that SOEs use bank loans, loans from government funding
agencies and/or international financial institutions as a source of financing. On
the contrary, studies by OECD (2005: 21) and Balmaceda et al. (2014: 47–48)
found that debt financing in the form of bonds and notes was the common type
of debt financing used by SOEs
The findings of this study revealed that the most commonly used types of debt
financing are short-term lease financing (83%), long-term lease financing and
short-term loans from shareholders (72%). The other popular type of debt
financing appeared to be long-term loans from shareholders ranked at 48%, while
there was an insignificant use of bank loans and bank overdrafts ranked at 15%
and 2% respectively. Apart from the above sources of pure debt financing, there
were other significant types of liabilities used by MoEs, such as trade and other
payables (89%) and taxation and VAT payables (49%). Consumer deposits
appeared to be insignificant and less popular with a ranking of 32% and project
funds payable at 28%. The findings further showed that MoEs had other
significant obligations in a form of provisions, and these include current provision
at 75%, employees’ retirement benefit at 54%, while long- and short-term
deferred income were insignificant at 32% and 28% respectively.
109
It was also very interesting to note that deferred tax, at 15%, was the smallest
liability of MoEs during the period of the study. Given the findings in Table 5.2, it
appeared that, during the period of the study, there were unique types of debt
financing, such as provisions, as well as other liabilities or obligations related to
total debt financing. It can, thus, be concluded that short-term lease financing,
long-term lease financing and short and long-term loans from shareholders were
the most commonly used sources of debt financing, while bank loans and bank
overdrafts were the least used source of debt financing by MoEs within the CoJ.
This finding is in line with the legislative framework prescribed in the Municipal
Finance Management Act 56 of 2003 of National Treasury.
The fifgure below summarises the types of debt finance used by MoEs within the
CoJ.
Figure 6.1: Total liabilities of MoEs
Source: Researcher’s own compilation
110
6.3.2 The extent of debt substantively preferred by MoEs and the
acceptable level of debt for MoEs
The results of this study demonstrate the trends and behaviour relating to the
extent of debt and the acceptable debt level among MoEs within the CoJ. The
preferences of debt in this study are demonstrated by their use within the MoEs.
The acceptable debt level and its limits are measured by the entity’s multi-year
business plan referred to in section 87 (5) (d) of the MFMA, which is determined
by both shareholder and the municipal entity in the budgeting process. The findings revealed a certain level of consistency in the behaviour of the total debt ratio
from 2011–2015. This finding, in Table 5.4, suggested that the extent of the total
debt ratio of MoEs fluctuated during the period of the study with a stable average
of 72%. This corroborates the findings of Wright (2004: 573), Frank and Goyal
(2005: 156) and Lemmon et al. (2008: 1584) that echo the fact that, in the end,
debt levels of entities appear to be stable. According to Frank and Goyal (2005:
158), the stability of the debt-financing level resembles the trade-off theory. There
is discord in empirical findings as another researcher has argued that the pecking
order theory better explains the debt financing level where internal funds are used
over external debt (Nyamita 2014: 195).
Comparatively, in Table 5.4, short-term debt appeared to have grown to 58%
during the year 2015, while long-term debt declined to 13%. Despite this, Table
5.4 indicated that the acceptable total debt level of MoEs within the CoJ is based
on an average range of 71%, consisting of a short-debt and long-term debt of
49% and 22% respectively. The total equity level was fluctuating, with an average
of 28%, which is higher than long-term debt but below short-term debt. Despite
this, the above evidence in the study confirmed that, on average, the extent of
debt within MoEs for the CoJ followed the principle of the trade-off theory,
whereas in 2015 it could suggest that the pecking order theory that says which
internal funds are preferred over external debt was followed. Nevertheless, when
observing the overall trend of the results on Table 5.4 in the absence of 2015, it
could be concluded that the MoEs followed the trade-off theory that says debt is
preferred over equity within the CoJ during the period of the study.
111
6.3.3 Factors influencing debt financing of MoEs within the CoJ
The main objective of the study has been to investigate factors that influence the
debt financing of MoEs for the CoJ. The results of previous, similar studies
conducted in various sectors identified common factors, such as profitability, size,
asset tangibility, entity growth, risk, corporate tax, liquidity, interest rate, inflation,
etc. to have an influence on debt financing (Huang & Song, 2006: 16; Luscombe,
2009: 9; Lim, 2012: 191; Nyamita, 2014: 96 and Otieno, 2015: 41). The identified
factors in this study were chosen based on the past empirical studies. The factors
included size, age, profitability, business risk, the corporate tax, nature of assets
(tangibility), growth opportunity, inflation rate, economic growth, and the interest
rate. The results of the study are consistent with other studies conducted in local
and international, private and public, entities. In essence, the relationship and
impact were established by the setting and testing of hypotheses between
independent variables and debt financing (dependent variable). The results are
summarised in the sections to follow. While running the regression model to
analyse the relationships between debt and the various factors, the model
showed that 74% of the variation in debt financing was a result of the identified
independent variables. Following recommendations by Flake and Lawrence
(1998: 276), the other independent variables, which were identified in the first
regression analysis, were excluded from the panel data EGLS regression model
to get final results.
•
Interest rate
Regarding interest rate and debt, the results demonstrated that interest rate
generally influences the debt financing of MoEs positively. In other words, an
increase in interest rates cause higher borrowing costs, consequently, there is
less demands on goods and services because less spending. These results were,
however, found to be insignificant during the period of the study 2011–2015. The
findings does not resonate with the views by Luscombe (2010: 16) that says that
the high possible cost of financial distress overshadows the compensation of the
interest tax shield and so dejects borrowing. Luscombe (2010), however, noted
that sometimes an increase in base interest rate could yield a greater tax shield
benefit for entities that rely on debt finance.
112
Yet, according to Sibindi (2017: 18), such tax shield benefits can be enjoyed only by
entities financed by debt up to an optimum point where there is equality between the
present value of the interest shield and the present value of financial distress
(bankruptcy costs). In the report by Johnston (2019), cost of borrowing when interest
remains low, firms can borrow more easily. Low interest loans can finance business
growth and increase profitability, as companies can raise enough from new ventures
to pay interest on loans and have money left over for profits. In summary, a low
interest rate gives MoEs an opportunity to acquire more debt financing.
•
Profitability
The results confirmed a negative relationship between profitability and debt
financing. This relationship was found to be significant at 5% level of confidence.
These results are a confirmation that MoEs within the CoJ followed the pecking order
theory during the years being studied (2011–2015). According to pecking order
theory, it is presumed that entities with higher profitability will choose internal funding
to debt financing, and so the relationship between profitability and debt financing is
expected to be negative (Baltacı and Ayaydın 2014: 49). These results confirmed the
set null hypothesis of the study concerning profitability and debt. This is also a
confirmation of the Municipal Finance Management Act 56 of 2003 National Treasury
that mandates management and boards of directors’ limited delegation of powers to
participate in debt financing. In addition, managers of MoEs cannot engage in
borrowings without following the bureaucratic process of the municipality council as
well as the National Treasury. As a result, they must first make use of their retained
profits before borrowing externally.
•
Size
The results of the study highlighted a positive relationship between the size of the
entity and debt financing. This relationship was found to be insignificant. The positive
influence affirms the set hypothesis, and it is an indication that the more MoEs grow,
the more financial resources they require to finance their activities. This helps larger
MoEs to benefit from the debt interest tax shield.The findings of this study are
consistent with the remarks by Luscombe (2010) that a number researcher has
discovered a positive association between debt financing and size. Luscome (2010:
22) also noted that smaller entities had higher bankruptcy costs, while larger, more
diversified entities had a better capability to bear debt.
113
This contrasts with the views of researchers such as Titman and Wessels (1988: 14),
Rajan and Zingales (1995: 1451), Nyamita (2014: 173), and Baltacı and Ayaydın
(2014: 49), who observed a negative relationship between debt financing and size. If
one consideres the pecking order theory, the expectation would be that size would
be related to debt negatively. This is because the pecking order theory predicts that
larger SOEs that are diversified use less debt financing (Baltacı and Ayaydın 2014:
49). In summary, the results of the study suggest that MoEs for the CoJ did not follow
the pecking order theory of debt financing during the period of the study.
•
Economic growth
The economic growth has been proven to have a positive association with MoE’s
debt financing in the CoJ and the association is significant. As described above, a
positive relationship between economic growth and debt financing implies that,
growth in the economy allows the MoEs to borrow less and this is detrimental to the
borrowing. It is interesting to note that researchers, such as Huang and Song (2006:
32), Antoniou, Guney and Paudyal (2008: 73), De Jong, Kabir and Nguyen (2008:
1961), Foster and Young (2013: 6), Jõeveer (2013: 306), and Nyamita (2014: 166),
have argued that the growth on the economy causes an increase in financial distress
and results in agency costs of debt financing. Whereas other researchers Alfaro et
al. (2004) and Choong et al. (2010: 108) argued that, an increase in economic growth
warrants the acquisition of less debt by companies. In summary, the rise in the
economic growth measure by the consumer price index gives a leeway for MoEs not
to seek debt because they would be profitable and sustainable.
•
Age
The results of the study have also shown a positive relationship between the age of
the entity and its financial leverage (debt financing). Furthermore, the results
highlighted a strong, insignificant relationship. This suggests that firms that have
been operational for a long time generally acquire more debt provided they are credit
worthy. The results support the views by Abor (2007: 33) that say that, as the entity
grows, it establishes itself as a continuing business and consequently increases its
capacity to take on more debt and so age is positively related to debt. Luscombe
(2009: 78) disputes this and argues that a negative relationship exists between age
and debt financing. In fact, the author argues that older and surviving entities have
more retained earnings and so prefer to use their retained earnings rather than using
debt, as per the pecking order theory.
114
Even though there are contrary views by researchers, an important issue emerging
from these findings is that the age of MoEs in Johannesburg is one of the significant
factors influencing debt financing.
•
Business risk
The results of the study have shown a significant positive influence of business
risk on debt financing. The results support the pecking order theory that states
that one of the fundamental determinants of the capital structure of an entity is
the level of its risk. This is because the more an entity is exposed to such risk,
the more it is impelled to reduce its level of debt within its capital structure, which
is a confirmation of a pecking order theory (Abor 2007: 38). It is, however,
noteworthy that the findings of this study do not generally represent the findings
by other researchers. For instance, studies by Frank and Goyal (2003: 3),
Deesomsak, et al. (2004: 398), Antoniou, Guney and Paudyal (2008: 77), De
Jong, Kabir and Nguyen (2008: 1961), Frank and Goyal (2009: 32), Lim (2012:
197), Baltacı and Ayaydın (2014: 54) and Nyamita, (2014: 174) have shown a
negative relationship between business risk and debt financing. It is evident from
the results of this study that MoEs within the CoJ followed the pecking theory
from 2011 to 2015.
6.4
CONTRIBUTION OF THE STUDY
Despite the empirical evidence on debt financing, there has been limited or insignificant number of studies conducted in South Africa. This study has investigated the factors that influence the debt financing of MoEs for the CoJ. The study,
therefore, contributes to the body of knowledge and helps to fill the gap in the
literature based on three set objectives: (i) factors influencing debt financing; (ii)
types of debt financing used by the various MoEs; (iii) the extent of debt financing
substantively preferred and acceptable debt level by MoEs in the CoJ.
6.4.1 Factors influencing debt financing within MoEs for the CoJ
This study has established that there are six factors influencing the debt financing
of MoEs within the CoJ both positively and negatively. The factors include interest
rate, profitability, size, economic growth, age, and business risk. The findings
have shown that profitability significantly influence debt financing negatively.
115
In addition, business risk and economic growth significantly have a positive
relationship with debt financing. Whereas, size, age and interest rate have an
insignificant positive relationship with debt financing. The results suggest that
MoEs adopted the pecking order theory on profitability during the period 2011–
2015 within the CoJ.
Based on these results, one could encourage the policy and decision makers in local
government and its entities to follow the pecking order theory when considering debt
financing. In addition, other factors, including size and business risk, had a positive
influence on debt financing of MoEs. These factors demonstrated that MoEs pimarily
follow the pecking order theory, but sometimes lean towards the agency costs theory
(Nyamita, 2014: 213).
6.4.2 The types of debt used by the various MoEs
The findings showed that MoEs within the CoJ used different types of debt during
the year of the study. These debts varied between short and long-term debt. The
pure types of debt financing used included short-term lease financing, long-term
lease financing and short-term loan from shareholders, long-term loans from
shareholders, bank loans and bank overdrafts. The other types of liabilities used by
MoEs were trade and other payables, taxation and VAT payable, consumer deposits,
as well as provisions, such as current provision, employees’ retirement benefit and
deferred income. This information will assist decision makers, such as board
directors, the City Council and management to strike a balance with regard to the
different types’ debt financing used within the MoEs. Furthermore, this will help MoEs
to comply with the legislative framework prescribed in the MFMA, even though there
are bureaucratic processes, which make it difficult for MoEs to engage in borrowings
on their own initiative.
6.4.3 The extent of debt substantively preferred and acceptable debt
level by MoEs
The findings have indicated that, on average during the years of study, MoEs of
the CoJ relied more on debt compared to total assets. In fact, it was noted that
the borrowings throughout the period being studied were consistent, with MoEs
relying on short-term compared to long-term debt. It was also noted that some
entities, during the period of the study, were technically insolvent while others
116
were at high risk of been liquidated. There are other possible explanations for this
result. For example, the preference of debt was based on its usage, while its
acceptable level was measured by the limits set by MoEs in consultation with the
shareholders during the multi-year business plan and budget processes.
Decision makers within the MoEs should monitor the extent of borrowing carefully
and ensure that it is within the frameworks of its business plan.
Moreover, decision makers have the responsibility of making proper and
reasonable plans that will not expose MoEs to insolvency or the possible risk of
liquidation. Finally, the acceptable level of debt and its limits should be
streamlined and capped at a particular percentage, one that will not result in the
entities facing financial distress.
6.5
LIMITATION OF THE STUDY
As indicated above, the objective of the study was to determine the factors that
influence the debt financing for MoEs for the CoJ. The study was confined to the
MoEs within the CoJ in Gauteng Metropolitan Municipality. Given the availability
of literature in relation to debt financing, there was some limitation noted during
the interpretation of the results in the study. The first limitation was based on the
collection of primary data. The study intended to administer a questionnaire to all
CFOs and Finance Managers to clarify issues not covered in the annual financial
statements. Owing to a poor response rate from the targeted respondents,
however, the primary data was disregarded. From there, secondary data was
used as the main source of data.
Given the fact that the study focused only on MoEs within the CoJ, this could
present opportunities for future study. The study under review has received attention in the past owing to its relevance and importance in developed and
developing countries. There have, however, been limited research studies conducted in South Africa particularly on MoEs and SoEs. The fact that the study
was limited only to the CoJ, thus, means that the findings cannot be generalised
to be applicable to all MoEs and SoEs in South Africa
117
6.6
RECOMMENDATIONS
The empirical results established by the study clearly have a significant impact
on decision makers within MoEs for the CoJ. It is apparent that the study warrants
researchers,
boards
of
directors,
management,
finance
students
and
shareholders (parent municipalities) to consider these findings in their respective
areas, and as a base to carry out further research in this area. The recommendations of the findings of the study are, therefore, provided as follows:
•
It is apparent from the study that MoEs within the CoJ relied very much on
debt, particularly short-term debt. It was, however, also noted that some of
these MoEs were facing impending insolvency and liquidation, thus making
short-term debt an unfavourable option. It is, therefore, recommended that
decision makers could encourage MoEs to balance the use of debt instruments between short and long-term to reduce the risk of impending
insolvency. An important issue emerging from these findings is that it is the
responsibility of the shareholder to encourage MoEs to consider the balancing of both short- and long-term debt. In the context of this study, municipal
heads and managers can encourage MoEs to ensure that there is a balance
between short term and long-term debt.
•
Secondly, MoEs should implement a balancing approach by minimising the
risks of insolvency and maximise the direct benefits associated with borrowing. This will reduce the chances of exposure to the risk of bankruptcy or
insolvency.
•
The study also recommends that decision makers within the MoEs should
use short- and long-term leases and short-term loans from shareholders as
a major source of debt financing (parent municipality). On another hand,
these types of debts cannot be the only ones to be used by MoEs, without
looking at other capital market instruments, such bonds, notes, debentures,
to mention only a few.
118
•
Borrowings of MoEs are confined within the prescripts of Municipal Finance
Management Act, no 56 of 2003 of National Treasury. Management and the
boards of directors should, therefore, familiarise themselves with such legislative prescripts before seeking approval for borrowings from the parent
municipality or shareholder.
•
The findings clearly demonstrate that specific factor, such as the interest rate
influence debt financing positively. This implies that, when the interest rates
rise, MoEs may pay more in interest and debt-related costs. The study
recommends that MoEs use fewer bank loans because they attract high
interest rates and acquire less external debt during a rapid economic growth.
Instead, they can use loans from shareholders (parent municipalities) which
come as an interest free loan or with low interest payments.
•
It is recommended that MoEs with higher profitability should be encouraged
to follow the principles of pecking order, were entities with higher profits
should first make use of internal funds before using debt and new equity
financing.
•
The study further recommends that the board of directors and managements
should be empowered and given delegated authority to make borrowing
decisions on behalf of MoEs without being subjected to the prescripts of the
Municipal Finance Management Act, no 56 of 2003 of National Treasury. To
ensure consistency, however, they would need to comply with the Companies
Act 71 of 2008 section 8, which deals with financial assistance.
6.7
AREAS FOR FUTURE RESEARCH
The study has investigated the factors that influence the debt financing of MoEs
for the CoJ. The findings of the study have led to a number of recommendations
for further research, not only for researchers, but also for management and
boards of directors of MoEs. The findings identified six different factors, which
had an influence on debt financing of MoEs within the CoJ, from 2011 to 2015.
Precisely, the following recommendations are proposed for future research:
119
•
A similar study could be conducted and designed to make comparisons
among the various types of debt financing of different MoEs in Gauteng or in
South Africa at large.
•
A similar study could also be carried out with a larger sample size over a
longer period. This could allow for an extensive study of the factors that
influence debt in South African MoEs.
•
Other researchers could also investigate the factors influencing investment
decisions of MoEs/SOEs in South Africa.
6.8
CONCLUSION
The aim of the study has been to investigate the factors influencing debt financing
of MoEs within the CoJ. As mentioned before, there is a plethora of studies and
empirical literature dealing with debt financing and focusing on private and public
entities. Although the results of this study focused on MoEs for the CoJ, common
independent variables, such as interest rate, profitability, size, economic growth,
age, and business risk were found to be the main factors that influence debt
financing of MoEs within the CoJ. The study also revealed that borrowings in
MoEs are skewed within the legislative prescripts of the MFMA, which gives
management or board of directors minimal power to make binding decisions
regarding debt compared to private entities. Another interesting conclusion drawn
from the study is that MoEs with higher profitability levels use less debt. As a
result, there is less interest associated with debt. Briefly, it can be concluded that
MoEs for the COJ pursued the pecking order theory during the period of the study
2011–2015. These findings, summarised above, have confirmed that the
objectives and aim of the study have been met.
120
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APPENDIX A
ETHICAL CLEARANCE CERTIFICATE
142
143
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APPENDIX B
LANGUAGE EDITING CERTIFICATE
145
146
The Reverend David Swanepoel
BA (Rhodes), Hons BA, Hons BTh, HED (SA)
The Hermitage
P.O. 70729, The Willows, Pretoria, 0041, South Africa
Telephone and Fax +27 (0)12-8074256
Email: davidswanepoel@wol.co.za
22 June 2019
TO WHOM IT MAY CONCERN
This is to certify that I have completed the English Editing of the text of a dissertation to be
submitted in fulfilment of the requirements for the degree of
MASTER OF PHILOSOPHY IN ACCOUNTING SCIENCES
in the subject
MANAGEMENT ACCOUNTING
at the
UNIVERSITY OF SOUTH AFRICA
The dissertation is entitled
FACTORS THAT INFLUENCE DEBT FINANCING OF MUNICIPAL
OWNED ENTITIES: A CASE STUDY OF THE CITY OF JOHANNESBURG
by
OUPA MADALA GALANE
I am qualified to have done such editing, being in possession of a Bachelor’s
degree in English from Rhodes University, Grahamstown, an Honours Degree in
English and HED with English as prime teaching subject from the University of
South Africa, and having taught English to Matriculation, First Year University
Level, GCSE and A level in both South Africa and the United Kingdom of Great
Britain for over 40 years, as well as having been Senior (English) Associate Editor
of a national magazine for two years. I have edited Master’s Dissertations and
Doctoral Theses for several years for several universities and institutions in South
Africa and abroad as well as editing documents/papers for publication for various
publishing concerns and a number of international academics.
I trust that this declaration is satisfactory.
DAVID JOHN SWANEPOEL
147
APPENDIX C
TURNITIN REPORT
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150